Deriving Archetype Templates for Urban Building
Energy Models Based on Measured Monthly Energy Use
by
AR(HIVE
Julia A. Sokol
MASSACHUSETTS INSTITUTE
OF'rECHNOLOLGY
B.A., Mechanical Engineering
JUL 3 0 2015
Harvard University, 2010
LIBRARIES
Submitted to the Department of Mechanical Engineering
in partial fulfillment of the requirements
for the degree of Master of Science in Mechanical Engineering
at the
Massachusetts Institute of Technology
June 2015
@
Massachusetts Institute of Technology, 2015. All rights reserved.
Signature of Author ............................
Signature redacted
Departm nt of Mechanical Engineering
May 27, 2015
Certified by ..............
.....................
Signature redacted
Christoph R0Ti5Tiart
Associate Professor
Thesis Supervisor
Certified by .............
Signature redacted
C/
Leslie Norford
Professor
Signature redacted/'s
Reader
Accepted by .........................................................................
David Hardt
Professor of Mechanical Engineering
Chairman, Department Committee on Graduate Theses
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Deriving Archetype Templates for Urban Building Energy Models Based on Measured
Monthly Energy Use
by Julia A. Sokol
Submitted to the Department of Mechanical Engineering
on May 27, 2015 in partial fulfillment of the requirements
for the degree of Master of Science in Mechanical Engineering.
Abstract
Interest in urban energy modeling has grown among planners and policy-makers as more and more municipalities set targets for reduction of greenhouse gas emissions. Urban-scale building energy models
can help evaluate the efficiency of proposed district designs, consequences of building retrofit interventions, or energy supply options. Bottom-up models based on physical descriptions and engineering
calculations are the most versatile for modeling scenarios and evaluating results at high spatial and
temporal resolutions. Such urban building energy models (UBEMs) are typically created by grouping
buildings with similar properties into archetypes, which standardize many properties that are not
uniform in reality, such as occupancy-driven parameters. Since most UBEMs are validated using
aggregated, annual measured data, this standardization is usually adequate; however, for a more accurate model that considers end-use differentiation or seasonal variation, neither this standardization
nor this validation method are sufficient.
This work proposes a new methodology for archetype definition and customization using metered
monthly energy data. Customization is done by inferring certain parameters from the energy data
and estimating others probabilistically from parametric analysis. The methodology is developed and
tested on a case study of 453 low-rise residential buildings in Cambridge, Massachusetts. Four model
iterations are compared: single template, eight archetype templates, eight archetypes with individual
building customization, and the latter with the addition of parametric analysis and generation of
frequency distributions for unknown parameters. The results show an improvement in mean goodness
of fit from 46% with one template and 37% with eight templates to 18% for the final iteration. The
distribution of energy use intensities, as well as monthly electricity and gas profiles, approach observed
values closer with each iteration. The results also demonstrate that error metrics based on aggregated
annual consumption, commonly used for urban model validation, are not necessarily representative of
the model's fit on a monthly basis.
Thesis Supervisor: Christoph Reinhart
Title: Associate Professor of Building Technology
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Acknowledgements
I would like to express my deepest gratitude to my research advisor Christoph Reinhart for his
guidance, patience, kindness, and inspiration. I am also grateful to Professors Les Norford and Leon
Glicksman, the rest of the Building Technology Faculty, and Kathleen Ross, for their help and advice,
and for creating a supportive BT community.
This research is part of a larger series of urban modeling projects in the Sustainable Design Lab and
was performed in close collaboration with PhD candidate Carlos Cerezo Davila, whose insight and
experience was invaluable.
To all the talented Building Technology students who have shared their time and knowledge, both in
lab and outside of it-especially Aiko, Cody, David, Irmak, Jeff, Leo, Madeline, Manos, Tarek, and
Timur-you have made the past two years memorable. Thank you to all my other friends at MIT
and farther away (especially Katie and the rest of the EBM lab) and to my dear roommate Polina for
their love and encouragement. Thanks also to Meder, through whom I got to know MIT long before
I could ever imagine being a student here, and to Anton, whose spirit is still here in Cambridge.
Lastly, deepest of thanks to my dad, sister, and brother-in-law for their endless support. And to
Cal-without whom I wouldn't be here-for all the magic.
5
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Contents
Abstract
3
Acknowledgements
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Contents
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List of Figures
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List of Tables
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1
Introduction
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .
1.3 Thesis Goals and Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Background
2.1 Energy Modeling of Existing Buildings
2.2 Calibration of Building Energy Models
2.2.1 Calibration Assessment . . . .
2.2.2 Calibration Methods . . . . . .
2.3 Urban Building Energy Modeling . . .
2.3.1 Archetype Definitions . . . . .
2.3.2 User Behavior Modeling . . . .
2.3.3 Model Validation . . . . . . . .
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Proposed Methodology for Urban
3.1 Data Collection . . . . . . . . . .
3.1.1 Weather Data . . . . . . .
3.1.2 Energy Data . . . . . . .
3.2 Building Data . . . . . . . . . . .
3.2.1 Geometric Properties . .
3.2.2 Non-Geometric Properties
3.3 Template Generation . . . . . . .
3.4 Template Customization . . . . .
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Energy
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Modeling
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Contents
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of Parameters
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Inference of Parameters
Probabilistic Estimation
Execution . . . . . . . .
Validation . . . . . . . .
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4 Application of Methodology to Cambridge, MA Case Study
4.1 Data Collection . . . . . . . . . . . . . . . . . .
4.1.1 Weather Data . . . . . . . . . . . . . . .
4.1.2 Energy Data . . . . . . . . . . . . . . .
4.2 Building Data . . . . . . . . . . . . . . . . . . .
4.2.1 Geometric Properties . . . . . . . . . .
4.2.2 Non-Geometric Properties . . . . . . . .
4.3 Template Generation . . . . . . . . . . . . . . .
4.3.1 Constructions . . . . . . . . . . . . . . .
4.3.2 Internal Loads and DHW . . . . . . . .
4.3.3 Schedules . . . . . . . . . . . . . . . . .
4.4 Template Customization . . . . . . . . . . . . .
4.4.1 Inference of Parameters . . . . . . . . .
4.4.2 Probabilistic Estimation of Parameters .
4.5 Model Iterations . . . . . . . . . . . . . . . . .
Results for Cambridge, MA Case Study
5.1 Error M etrics . . . . . . . . . . . . . . . . . . . . .
5.2 Annual and Monthly Simulation Results . . . . . .
5.2.1 Monthly Comparison . . . . . . . . . . . . .
5.2.2 EUI Distribution Comparison . . . . . . . .
5.3 Parameter Distrib1tinn
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5.3.1 Inferred Parameters . . . . . . . . . . . . .
5.3.2 Probabilistically-Estimated Parameters . .
5.4 Results Summary . . . . . . . . . . . . . . . . . . .
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6 Discussion and Conclusion
6.1 D iscussion . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Parameter Uncertainty Reduction . . . . .
6.1.2 Generation of Improved Archetypes.....
6.1.3 Evaluating Consequences of Data Availability
6.2 Lim itations . . . . . . . . . . . . . . . . . . . . . .
6.2.1 Geometric Limitations . . . . . . . . . . . .
6.2.2 Modeling Simplifications . . . . . . . . . . .
6.2.3 Limitations of Results . . . . . . . . . . . .
6.3 Future Work . . . . . . . . . . . . . . . . . . . . .
6.3.1 Validation . . . . . . . . . . . . . . . . . . .
6.3.2 Methodology Refinement . . . . . . . . . .
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Sensitivity Analysis . . . . . . . .
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6.3.2.2 Hourly Energy Data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3.3 Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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A Energy Model Templates
A.1 Constructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.2 Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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6.4
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List of Figures
1.1
1.2
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U.S. energy flows in quadrillion Btu, 2014. [1] . . . . . . . . . . . . . . . . . . . . . . .
Historical energy consumption by end-use sector, in quadrillion Btu. [1] . . . . . . . .
Monthly energy consumption by end-use.sector, 2012-2014, in quadrillion Btu. [1] . . .
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2.1
Techniques used for estimating regional or national residential energy consumption. [2]
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3.1
Example of residential monthly energy use in Cambridge, MA.
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Tax parcels of the City of Cambridge. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histogram by total 2008 EUI, shaded by construction period (pre-1945, 1946-1980,
post-1980). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Histograms by 2008 EUI, separated into gas and electric use intensities. . . . . . . . .
Monthly energy use intensities for low-rise residential buildings in Cambridge. . . . . .
Building geometry generation process. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Energy use shaded by age category of building. . . . . . . . . . . . . . . . . . . . . . .
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Rendering of the Cambridgeport 3D model. . . . . . . . . . . . . . . . . . . . . . . . .
Annual results for baseline run with a single template assigned to all buildings. . . . .
Annual results for Run 1 with initial templates generated from annual data. . . . . . .
Annual results for templates customized to each building based on parameters inferred
from m onthly data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Annual results for lowest-error parametric runs with variable occupancy and setpoints.
Measured monthly gas and electric use intensity. . . . . . . . . . . . . . . . . . . . . .
Monthly simulation results for a subset of 200 buildings. . . . . . . . . . . . . . . . . .
Measured (shaded grey) and simulated (shaded green) EUI distributions for neighborhood, in W/m 2 : Run 0 (top left), Run 1 (top right), Run 2 (bottom left), Run 3
(bottom right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Distribution of peak electric load intensities. . . . . . . . . . . . . . . . . . . . . . . . .
Distribution of peak domestic hot water flow. . . . . . . . . . . . . . . . . . . . . . . .
Distributions of AC use (left) and heating system efficiencies (right). . . . . . . . . . .
Distribution of occupancy density per square meter for GOF < 10. . . . . . . . . . . .
Cooling (left) and heating (right) setpoint distributions for GOF < 10. . . . . . . . . .
Goodness of fit by individual building for every run. (Note that the vertical axis is
truncated, so the highest error points are not displayed.) . . . . . . . . . . . . . . . . .
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3D models of Cambridgeport with and without LiDAR data.
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List of Tables
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Error limits for whole building calibrated simulation. [3, 4] . . . . . . . . . . . . . . . .
4.1
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Summary of Cambridge residential dataset
Linear regression results. . . . . . . . . . .
Heating system efficiencies. . . . . . . . .
Parametric analysis settings. . . . . . . .
Energy model iterations. . . . . . . . . . .
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Summary of validation results by run, time period considered, and aggregation level. .
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Chapter 1
Introduction
1.1
Motivation
In 2014, the Residential and Commercial sectors were responsible for 40.5% of the total energy consumed in the United States, adding up to 11, 685 terawatt-hours or 39.9 x 1015 Btu (Figure 1.1) [1].
The energy consumption of these two sectors can be ascribed almost entirely to buildings-the electricity and fuel used for electrical appliances, lighting and space conditioning of all private living
quarters, business and institutional facilities. (The commercial sector also includes energy consumed
by street lighting and sewage treatment facilities, but their contribution is comparatively small [1]).
This is not just a U.S. phenomenon: globally, residential and commercial buildings are responsible
for 30-40% of final energy consumption, and about a third of the world's greenhouse gas emissions.
Building-related consumption continues to grow as developing countries increase in population and
standard of living.
Fortunately, much of this consumption is avoidable through the implementation of appropriate energy efficiency measures. For developing and urbanizing areas, this entails designing sustainable new
neighborhoods with high energy efficiency standards. For developed countries with low building stock
growth, where the majority of existing buildings were constructed before building energy codes went
info effect in the 1970s, policy makers need to implement smart strategies for building retrofits.
In response to these challenges, state and municipal governments in the United States and around the
world have started instituting targets for reduction of greenhouse gas emissions from the buildings
sector. Since the first step in managing progress is measuring baseline performance, these targets
have led to the establishment of the first energy disclosure laws. These laws require all buildings
above a certain floor area to submit annual energy consumption records of their energy consumption
to city governments. This information is then used to benchmark the performance of buildings within
the same function and identify areas for improvement. Some cities have combined energy disclosure
12
Chapter 1. Introduction
FIGURE 1.1:
U.S. energy flows in quadrillion Btu, 2014. [1]
laws with additional requirements for conducting building energy audits and retro-commissioning at
certain time intervals. However, so far only fourteen cities and two states' in the U.S. have energy
disclosure policies in place. Most of them apply only to commercial buildings (some include multifamily
residential), and half require this information only from buildings with floor area above 50,000 square
feet (4,645
in 2 )
[5].
Consequently, these regulations exclude the majority of low-rise residential structures. Individually
such homes might have insignificant energy demands compared to those of large commercial buildings;
yet, taken together, homes with one to four residential units comprise 200 billion square feet, compared
to 87 billion square feet for all commercial buildings [6, 7]. Historically, the residential sector has
consistently consumed more energy than the commercial sector (Figure 1.2). In recent years, monthly
energy use in the residential sector at peak heating periods has even reached that of the industrial
sector (Figure 1.3). Within the residential sector, low-rise (i.e., four units and under) buildings make
up 89% of total residential floor area nationally [7]. This percentage varies by municipality depending
Even in a city as dense as New York, however, 1-4 family
residences makes up around 41% of the city's total residential floor area [8]. Therefore, it would be
valuable to understand and model this significant component of the building stock in greater detail
on the population density of the city.
than has been done so far.
Cities: Seattle, Portland, Berkeley, San Francisco, Santa Fe, Austin, Minneapolis, Chicago, Atlanta, Washington,
D.C., Philadelphia, New York, Boston and Cambridge. States: California and Washington.
13
Chapter 1. Introduction
Total Consumption by End-Use Sector, 1949-2014
40-
Industrial
30-
Transportation..
-
..........
Residential
Commercial
-
10--
1950
1960
1955
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
FIGURE 1.2: Historical energy consumption by end-use sector, in quadrillion Btu. [1]
Total Consumption by End-Use Sector, Monthly
4-
Transporation
Pr
3-
Industrial
3-
2-
Residential
Commercial
1-
J
A M
J J A
2012
S O
N D
J F MAMJ
J A
2013
S O
N
D
J F M AMJ
J A
2014
SO
ND
FIGURE 1.3: Monthly energy consumption by end-use sector, 2012-2014, in quadrillion Btu. [1]
On top of its large contribution to global energy use, the residential sector presents a series of challenges for energy monitoring and regulation.
First, it is set apart from commercial and industrial
facilities due to highly decentralized ownership and disaggregation of energy use by tenants within a
building. Utility companies supplying residential customers are the only source-besides the tenants
themselves-that can provide information on their customers' energy use; however, privacy concerns
generally inhibit utilities from sharing information that links energy use of an account to a specific
address. 2 Utility companies are more willing to provide data aggregated by block or zip code, and this
2
Some progress is being made on this front: more and more utilities are participating in the Green Button program [9],
which allows customers to view and download their own historical usage data in a consistent format and to share it
with third-party applications on an opt-in basis. However, the effectiveness of this practice depends entirely on tenants'
desire to participate.
14
Chapter 1. Introduction
has recently been used by some researchers to create block-level maps of energy consumption [8, 10, 11].
While energy maps at this scale might be useful for urban planning, they are not particularly helpful
for identifying underperforming buildings or informing owners which retrofits to pursue.
Furthermore, while many commercial buildings have the capability to track their energy use on an
hourly or sub-hourly basis through submetering, residential energy tracking lags behind, since submetering at that scale is considered cost-prohibitive. In addition, commercial buildings can exert a
high degree of control over the operation of equipment and lighting through Building Automation
Systems (BAS), so the building manager can relatively easily make adjustments to affect anything
from a specific room to the entire facility. In residential buildings, equipment or lighting controls are
generally much more primitive and more heavily influenced by user behavior, which varies greatly
based on the nature of the occupants and is very difficult to track.
1.2
Research Question
Urban-scale building modeling has emerged as a way to understand current trends and predict future ones in energy consumption and greenhouse gas emissions in cities. Various approaches have
been taken to formulate such models, from top-down statistical analyses to bottom-up engineering
models built from physical building descriptions. While top-down models are useful for describing
existing conditions, they are limited in their predictive ability to very small variations from the status
quo. The bottom-up engineering model approach is the most versatile for modeling various scenarios
and evaluating results at high spatial and temporal resolution. Such urban building energy models
(UBEMs) can be used by urban planners and policy-makers to evaluate the energy-efficiency of a proposed neighborhood, assess impacts of potential retrofits on an existing district, or compare possible
energy supply alternatives.
Due to the difficulty of gathering information on large quantities of buildings, bottom-up UBEMs are
typically generated by grouping buildings with similar properties and defining detailed inputs just for
one characteristic building, or archetype, per group. These archetype properties are then combined
with some representation of the buildings' geometries. Thermal simulations with location-specific
weather conditions are then performed, either for each building (if their geometric shapes are defined
individually) or for each archetype building with consequent scaling up by floor area of all buildings
within the type. If it is an existing neighborhood and historical energy data is available, the outputs
of the energy model are then validated, typically by comparing to the neighborhood's annual energy
consumption.
This general method has been followed by many researchers with some variation within each step.
However, it has limitations when attempting to create a model for any subsequent analysis requiring
more detail than simply annual, aggregated energy demand. Neither this method of defining building
15
Chapter 1. Introduction
properties nor this validation procedure is sufficient to ensure a model representative of actual seasonal
energy variations or end-use proportions. In particular, three main limitations in urban building energy
modeling have been identified and are addresses in this thesis:
1. Lack of a systematic methodology for defining building archetypes for urban energy models: Currently, archetype classification is usually done somewhat arbitrarily, based on expert judgment
or classifications used in prior research. Without linking archetype definitions to measured data,
there is no way of determining which variables in the modeled building stock actually affect energy use and, conversely, no way to demonstrate that the archetypes, once defined, create groups
with homogeneity in energy profiles.
2. Uniformity in modeling occupancy parameters: Since occupant behavior is highly variable and
information on it is rarely available, urban models-even ones that are meant to provide results
on an hourly basis-tend to assign the same occupancy-related parameters to buildings of the
same function [12, 13]. This includes parameters that are directly under the influence of the
occupants rather than the physical properties of the building, such as operating schedules,
occupant densities, or thermostat setpoints. Such uniformity of behavior is unrepresentative of
reality and becomes an issue in energy modeling since differences in occupancy have been shown
to be responsible for a large portion of the variance in energy consumption, especially among
residential buildings.
3. Reliance on annual, aggregatedmeasured data for model validation: Little validation of UBEMs
compared to measured data has been done to date [14]; those that have been validated did it
by comparing their results to annual energy use quantities. A matching annual value, however,
does not provide any guarantee that the attribution of this energy to different end-uses was
done correctly. Some models do break energy down into end-uses or look at just a single end-use
when validating; even then, it is unknown whether the model's results match well at shorter
time scales. Furthermore, validation is typically done at the aggregated scale of an entire block,
neighborhood or city. Few researchers have gone into greater detail and checked their urban
models against individual buildings' energy uses; those that have report individual building
errors that are several times larger than aggregated ones.
1.3
Thesis Goals and Outline
The aim of this thesis is to address the limitations identified above for modeling urban building energy
use through an improved bottom-up approach that employs data analysis to reduce uncertainty. It
explores the extent to which an UBEM can be refined when sub-annual, individual-building energy
data is available for a neighborhood. Specifically, its goals are to:
16
Chapter 1. Introduction
1. Present a data-based methodology for the creation and refinement of building archetype templates.
2. Probabilistically estimate indeterminate occupancy-driven parameters through a parametric
analysis; use the resulting probability distributions to account for uncertainty in these parameters in the given or similar neighborhood.
3. Validate the model against both annual and monthly data, on an individual building basis and
in aggregate; compare results to determine advantage of monthly data availability.
Chapter 2 provides a background on current urban modeling practices. The general proposed methodology for urban building energy modeling and refinement is developed in Chapter 3. Chapter 4
describes specific details of the application of this methodology to a case study of Cambridge, Massachusetts. The results of the case study are presented in Chapter 5. Chapter 6 discusses the main
conclusions of this research, its limitations, and plans for future work.
17
Chapter 2
Background
In its aim of addressing uncertainty and increasing accuracy in urban building energy models (UBEM),
this thesis builds upon prior work in urban energy modeling, which in turn arose out of the building
energy modeling (BEM) field. This chapter summarizes BEM practices for existing buildings, focusing
on ways that have been used for calibration of models to measured data. In UBEM, calibration as
such has not really been done due to the lack of granular data, both of building properties and of
measured energy. Rather, comparison to measured data in UBEMs is usually termed validation and
is done primarily on an aggregated basis.
2.1
Energy Modeling of Existing Buildings
Building Energy Modeling (BEM) has been used since the 1970s for predicting electric and fuel
demands of buildings. These models can be divided into two broad approaches: forward models
and data-driven (inverse) models [15]. Forward models-also called physical models-are used in
the design stages of buildings before any measured data is available. They rely on inputs of climate
data plus the building's geometry, constructions, occupancy profiles, zoning and systems, as designed.
These inputs are used in energy balance calculations that determine heating and cooling loads for the
spaces at certain time intervals, which are in turn fed into models of system components that serve
to satisfy the calculated loads. Forward models are most commonly created using software developed
specifically for BEM, which use detailed algorithms based on physical principles of heat and mass
transfer. These include both government-funded (DOE-2, EnergyPlus) and commercially-developed
simulation engines (TRNSYS, ECOTECT, IES-VE, among others).
For the simulation of existing buildings, however, forward models are not sufficient: so much of the
energy use is determined by a building's operation that forward models are generally not representative
of real post-occupancy conditions. When a building's measured energy values for a certain time period
18
Chapter 2. Background
are available, data-driven (or inverse) models need to be used. Inverse models use the known outputs
to determine the system's input parameters and their mathematical relationship to the outputs. They
can be broken down into three subtypes:
o Black-Box Models: This is a purely mathematical approach that relies on identifying a model
to link measured energy use to a set of building properties and weather parameters. Multiple
Linear Regression has been used most often due to ease of interpretation; however, more complicated models, such as Multistage Regression, Artificial Neural Networks (ANN), Support Vector
Machines (SVM) and others have been used in recent years.
o Gray-Box Models: This method uses a combination of physical modeling and statistical methods. A physical (usually simplified) model is created to represent actual features of a building,
while statistical analysis with measured data is used to identify specific building characteristics.
o Calibrated Models: This method uses BEM software to create a physical model of a given
building, then tunes the model's input parameters to match measured energy use as closely
as possible. This method tends to be the most time-consuming of the three but provides the
most flexible result. The accuracy of the model, however, greatly depends on the method and
the time step used. (Calibrating to 8,760 hourly values, for instance, will inevitably result in a
higher-accuracy model than to a single annual value.)
This thesis uses a variation of the Calibrated Model approach due to its potential for automation,
accuracy of physical process representations, and versatility in applications. The following section
expands on procedures that have been used for calibrating models of existing buildings.
2.2
Calibration of Building Energy Models
The calibration approach to modeling existing buildings is typically used when detailed building
energy data is required, such as for examining the effects of proposed retrofits to a building or testing
different control schemes. The advantage of a calibrated model as opposed to a black-box or graybox one is that, while all three approaches can yield outputs that would closely match observed
energy consumption, only the calibrated model is entirely based on physical phenomena; thus, if
the calibration is sufficiently accurate, it offers the greatest flexibility in scenarios the model can
predict. The BEM engines used in calibration are generally evaluated according to ASHRAE Standard
140 [16 on their ability to model specific components consistent with established requirements. Ones
that perform well can be assumed to faithfully represent physical processes within a building, at least
insofar as every model is a simplification of reality. Therefore, if the simulation algorithms are assumed
to be "correct" (i.e., they perform engineering calculations using accepted equations and numerical
19
Chapter 2. Background
methods) and the observed energy consumption data is assumed accurate as well, what remains to be
determined during calibration are the inputs to the simulation engine.
However, BEM calibration remains an under-defined and over-parametrized problem without a unique
solution. The difficulty stems from the fact that many disparate properties of a building can have
similar effects on energy use (e.g., both thicker wall insulation and higher occupancy density can lead
to a decrease in heating load), while others have effects that cancel each other out to various extents.
In such cases, there can be an unlimited number of input parameter vectors that result in the same
or comparable calibration errors. The determination of which one of these solutions, or input vectors,
is the "most correct" is generally left up to the judgment of the practitioner. Furthermore, a model
is usually considered sufficiently calibrated if its error (defined below) is within a certain maximum
bound; the resulting model's parameters are rarely validated by inspection against the actual building,
even when it is feasible.
2.2.1
Calibration Assessment
The two official standards most often referred to when assessing the quality of a calibrated energy model are ASHRAE Guideline 14 for Measurement of Energy and Demand Savings [3], used
in the United States, and the International Performance Measurement and Verification Protocol
(1PMVP) [4], used internationally. Both of these provide techniques for quantifying effects of energy conservation measures (ECMs) on existing buildings, with Calibrated Simulation as one of the
options. Two statistical indices are used in these standards for assessing calibration quality: the
normalized mean bias error (NMBE) and the coefficient of variation of the root mean square error
(CVRMSE). These are defined as follows:
)
(2.1)
-
NMBE = 100 x Z=1(Y
n
CVRME ==100
CVRMSE
--
x
"=(2)
1(yi -
Qi)2(2)
where n is the number of measurements in the calibration period, yi is the observed energy value for
the ith period, i is the simulated value for the same period, and 9 is the mean of the n observed
values. The CVRMSE is a measure of how well the model fits the measured values at each interval;
the NMBE measures the error between the means of the simulated and measured data for the entire
time period.
ASHRAE Guideline
14 and IPMVP declare a model to be 'calibrated' if it meets the criteria in
Table 2.1 for these two metrics, varying based on the time interval used in the comparison. While
these criteria provide some guidance on how far total simulated energy values can be from measured
ones, it is relatively vague. They do not require separation between electricity and fuel used by the
20
Chapter 2. Background
building and do not place any constraints on matching energy by specific end-use. This allows room for
inconsistencies between the model and the actual building even when the maximum error thresholds
are met.
Metric
NMBE
CVRMSE
TABLE
2.2.2
ASHRAE 14
Monthly Hourly
10%
5%
30%
15%
IPMVP
Monthly Hourly
5%
20%
20%
-
2.1: Error limits for whole building calibrated simulation. [3, 4]
Calibration Methods
While ASHRAE Guideline 14 and IPMVP prescribe metrics to assess the quality of a calibrated
model, they do not provide much guidance on the actual steps of the calibration process, leaving it up
to the practitioner to determine the best method. As yet, there is no widely-accepted general calibration procedure, which makes the process largely non-replicable and inhibits inter-model comparisons.
As Coakley et al. point out, "many of the current approaches to model calibration rely heavily on user
knowledge, past experience, statistical expertise, engineering judgment, and an abundance of trial and
error" [17].
In an attempt to provide additional guidance for the calibration process, many researchers have
proposed their own methodologies. These feature a variety of approaches, ranging from manual
graphical-based methods to ones rooted in statistical analysis and optimization. The latter type typically requires less user judgment and thus offers greater opportunity for automation of the calibration
process. In the context of urban energy simulation, automation is essential, as large numbers of
buildings make manual calibration time-prohibitive.
The first comprehensive review of calibration methods was performed by Reddy et al. [18, 19] as
part of ASHRAE's Research Project 1051. Reddy synthesized the most successful procedures into a
partially-automated multi-step methodology employing sensitivity analysis and two-stage optimization. Importantly, Reddy advocated choosing a set of most plausible solutions, rather than a single
one, as a way to account for the uncertainty associated with non-uniqueness of solutions. Reddy's case
studies showed good agreement with measured data: Goodness of Fit values, defined as a weighted
combination of CVRMSE and NMBE, were within 1-2%. However, a later paper by Gestwick et
al. [20] tested Reddy's method on a 12,000 m2 office building and showed less satisfactory results,
with a monthly NMBE of 8.4%, even after significant manual adjustments. Since Reddy's analysis,
many other automated calibration methods have been proposed. They can generally be classified
under one of the following types:
21
Chapter 2. Background
o Optimization involves the minimization of an objective function-usually a measure of calibration error-using one of many optimization algorithms to search through the parameter vector
space.
o Bayesian Calibration assumes known prior distributions for the variable parameters. These
priors are updated using known outputs (measured data) so that their posterior distributions
reflect this added knowledge.
o Meta-Modeling involves the creation of a simplified, algebraic or machine-learning-based
model based on the original engineering building energy model created with BEM software,
with the goal of obtaining the same results but at a much lower computational cost.
More detailed descriptions of the latest calibration methods can be found in recent reviews by Coakley
et al. [17] and Fabrizio et al. [21]. Most of these methods have not been tested by other researchers;
hence, their general applicability to various building types has not been confirmed.
When scaling up from a single building to a neighborhood or city with hundreds or thousands of buildings, data availability and computational requirements become prominent issues in model calibration.
For this reason, researchers in urban energy modeling have been obliged to adapt the single-building
approaches described above or develop alternatives. An overview of these is presented in the following
section.
2.3
Urban Building Energy Modeling
Urban Building Energy Modeling (UBEM) is a growing field of research with a range of applications:
o For districts, cities or countries that are evaluating energy efficiency measures for the building
sector, calibrated urban models provide a way to estimate effects of potential policies or to assess
the feasibility of greenhouse gas reduction targets.
o For a campus or neighborhood considering the installation of a district heating system or distributed generation, an UBEM can provide information on hourly loads for the district, separated
by end-use.
o UBEM can provide predictions for utilities forecasting future electrical demand for new or growing districts.
Urban building energy modeling is divided into two major approaches that have been labeled "topdown" and "bottom-up." The top-down approach consists of using known aggregate energy consumption for a given region and time period (usually annual) and subdividing it into portions that are
22
Chapter 2. Background
attributed to specific groups of buildings (e.g., to all buildings of one function, to each zipcode in a
city, etc.). The bottom-up approach attempts to match the same aggregated energy consumption by
taking the inverse route and creating models at an individual building level, then summing up the results for all buildings in the set. While both approaches aim to describe the same energy consumption,
top-down models are limited in the sense that they are trained using historical data on consumption
levels, building conditions, economic indicators, etc. Being entirely statistical in nature, they are
limited in their predictive ability to very small variations from the status quo and so cannot model
consequences of technological advances, changes in construction practices, etc. [2]. The bottom-up
approach does not have that limitation. A further advantage is that the energy consumption can from
the very beginning be separated into end uses, and that results can have high spatial resolutions.
Two sub-methods exist within the bottom-up approach: the engineering method (EM), which creates
engineering models of buildings using either BEM software or simplified thermal models, and the
statistical method (SM) which uses black-box modeling. Figure 2.1 from Swan et al. [2] shows the
breakdown of methods that have been used for modeling residential energy consumption; these are
generalizable to urban energy models for any building type.
Resiential
Energy
Consumption
Econo=etri
Technological
Engineering
Statistical
Conditon.lPmhb
Regression
demand
Nural network
Diitop
n
Ardheyp
Sample
FIGURE 2.1: Techniques used for estimating regional or national residential energy consumption. [2]
A typical bottom-up urban building energy modeling procedure involves the steps of data collection,
conversion of data into energy model inputs, model execution, and validation of results by comparison
to measured data. Data access is typically the limiting factor in how detailed an UBEM can be and
how closely it can be validated. Building properties may be available on a large-scale basis thanks
to housing, insurance, or property tax assessment databases; however, this data is rarely accurate or
complete enough to serve as inputs to energy models. Energy data is even more difficult to obtain in
large quantities due to privacy concerns. Most UBEMs end up relying on annual, district- or city-wide
energy totals to validate their results. This reliance on spatially-aggregated and temporally-coarse
data embeds large uncertainties into both urban model creation and model validation.
23
Chapter 2. Background
2.3.1
Archetype Definitions
Bottom-up engineering models use building characteristics to use as inputs to energy simulation
algorithms. When making an energy model of an existing building, these characteristics are typically
collected by examination of architectural and mechanical drawings and an in-person energy audit.
For an urban model with hundreds or thousands of buildings, this procedure is infeasible, so modelers
must employ some level of generalization. The usual way to generalize this step is by separating the
building stock into homogeneous groups and assigning the same building properties to all buildings
within a group. This method automatically assumes that there are certain building properties that
can explain much of the variance in energy consumption between groups of buildings.
The non-
geometric, non-climatic properties most often used to differentiate buildings by group include age
(most common), use, HVAC system, and construction types.
Yet, when measured energy data for individual buildings from the area being modeled (or from another
location, similar with respect to geography, demographics, and construction practices) is unavailable,
the rationale for defining archetypes remains somewhat arbitrary. It is usually based on expert
judgment, general knowledge of construction practices, or methods used in prior research. In this
situation, there is no way of determining which variables in the modeled building stock actually affect
energy use, or of demonstrating that the defined archetypes create appropriate groups with similarity
in energy consumption.
Aksoezen et al. [22] explored whether classification of buildings by age and use is indeed a meaningful
way to generate archetypes. They noted that several studies had thrown doubt on the common
classification of buildings by construction age and dwelling type, as variations within the same class
were often greater than between classes. To check their hypothesis, Aksoezen et al. performed an
analysis on about 20,000 buildings in Basel, Germany, for which energy data for the year 2011 was
available. They used annual natural gas consumption intensity as a measure of energy performance
and demonstrated that for the given dataset buildings constructed in the period 1921-1979 used more
gas than those before 1921 or after 1980, so in this case age was indeed appropriate as a differentiation
factor.
The impulse to use building age as a classification variable is understandable, since it can automatically reveal further information about the construction practices and- materials used in the building.
However, this information is not deterministic and cannot solely be relied on for archetype definitions. A building's construction date contains no information on whether any refurbishment, such as
improvement in insulation or replacement of original windows, has been performed. Orehounig [23]
confirmed this with a case study of 100 buildings in a village in Switzerland, which were divided into
7 templates by age and modeled with the CitySim energy simulation environment. The authors noted
that energy predictions for buildings less than 30 years old were much more accurate than for the
older stock, whose energy consumption was highly variable.
24
Chapter 2. Background
Data-based approaches to archetype definitions have been limited. Aksoezen et al. checked just one
possible archetype distinction on their data set. Famuyibo et al. are among the few researchers
who applied measured energy data to create archetypes for a building stock (residential homes in
Ireland) [24]. The measurements came from the Energy Performance Survey of Irish Housing, which
contained energy use and physical characteristics for 150 typical Irish dwellings. They conducted a
literature review to identify which variables had been considered significant to energy consumption
in prior studies, followed that with a multivariate linear regression for the given measured dataset,
then looked at the frequency distributions for each of the significant variables and chose representative
values of these variables based on histogram peaks. The thirteen resulting archetypes were said to be
representative of 65% of the Irish housing stock.
2.3.2
User Behavior Modeling
The most frequently cited limitation of bottom-up UBEMs is the uniformity of occupancy modeling. Most urban models tailor occupancy and schedules to the building type but do not vary the
profiles within one type of building. This means that all residential spaces are modeled with the
same occupancy, setpoints, and lighting and equipment schedules. However, it has been shown that
user behavior is among the chief drivers of energy use in the residential sector [25]. Since most urban models only validate their results on an annual basis, discrepancies in occupancy and schedules
might not be obvious due to averaging when aggregated. However, when these models attempt to use
annually-validated results to predict spatially-distributed daily or hourly energy use within a city or
neighborhood, the idea that every building shares the same user behavior is certainly erroneous.
Strzalka et al. [26] compared ten similar apartments within the same multifamily building by annual
heating energy intensity; they showed variation between 30 and 90 kWh/m 2 in the same year. The
same study, when comparing a heating model for 300 residential houses that assumes the same user
behavior among buildings, showed that the model-influenced primarily by the buildings' geometrieswas not able to account for the large variations in energy use among these buildings (EUIs ranging
from 20 to 90 kWh/m 2); these large variations can only be attributed to user behavior. Based on
previous sensitivity studies stating that thermostat setpoint is among the most influential parameters
on heating demand, the authors adjusted setpoints according to a normal distribution around a mean
of 68'F. The addition of this degree of freedom resulted in much closer correlation between simulated
and measured values.
The Household Electricity Survey conducted in the United Kingdom in 2010-2011 [27] illustrates
some of the issues related to the unpredictability of residential energy consumption. This survey-the
most detailed one ever undertaken of electricity consumption in UK homes-monitored 250 buildings,
of which 26 were monitored for an entire year and the remainder for one month each on a rolling
basis. Monitoring was done with meters added to the buildings' central distribution boards and to
25
Chapter 2. Background
some individual appliances in order to disaggregate different end-uses. The researchers analyzed the
electrical base loads of different end-uses (the minimum hourly value over a day) and found huge
variation between individual households; a small portion of sample houses had base loads several
times higher than their peers. For example, about 60% of the total IT category base load could be
attributed to just 12% of the households. Similar contrasts were noticed for lighting and audiovisual
appliances. In the audiovisual category, 17 households used under 100 kWh per year, while the top 22
used more than 1000 kWh per year-ten times the amount of the lowest users. One limitation of this
study is that energy use between households was compared by building and not normalized by floor
area; normalization would likely have reduced the variation in demand and annual consumption to a
certain extent. However, the variance in floor area and number of household members still would not
fully account for the variance in energy use, indicating the large role played by occupant behavior.
Accounting for user behavior could be done in various ways. Research has been done on generating
behavior schedules based on stochastic algorithms. Alternatively, multiple researchers have noticed
correlations between demographic factors such as household income and energy use ([28], [29]). This
suggests the possibility of using census data to create appropriate schedules or appliance loads based
on population. In a different approach, travel surveys could be used to inform occupant schedules
([30], [31]). Keirstead and Sivakumar [30] used activity-based modeling to simulate hourly electricity
and fuel demands. Activity-based modeling is a type of integrated land use and transportation modeling based on microsimulation of agents' schedules with regards to activity and location throughout a
time period. Keirstead's model assigned schedules to each of 65,000 statistically-representative agents
(population members) and simulated the electric and gas demand by time of day across 391 spatial
zones in the city of London. One of the limitations of their study was a lack of variation in the domestic activity profiles, since the travel survey used to build the model did not contain this information;
therefore, it would not help in predicting residential energy consumption.
2.3.3
Model Validation
Calibration, which has long been used in the building energy modeling practice, is associated with
a range of difficulties even when applied to a single building (Section 2.2). An urban model greatly
increases not only the quantity of buildings that need to be calibrated, but also the uncertainty of
initial, pre-calibrated model descriptions. For these reasons, as well as the difficulty of obtaining
energy data for individual buildings at shorter time intervals, the practice of urban modeling has so
far mostly relied on annual, aggregated energy measurements for model validation.
Calibration of groups individual building models within an urban context to sub-annual data has not,
to our knowledge, been performed. One recent work by Sehrawat et al. [32] did compare errors in
energy models at monthly intervals for a block with 27 office buildings in Los Angeles, CA. (The
energy data was provided by the LA Department of Water and Power.) Their model showed an error
26
Chapter 2. Background
variation of 11-23% by month for the aggregated block energy consumption. Errors by individual
building were reported only annually and ranged up to 14% of EUL.
Fonseca et al. [33] created their own detailed integrated model of energy use in buildings instead of
using BEM software and validated it against a neighborhood of 23 buildings. While their integrated
model can predict energy use at hourly intervals, its results were not validated on a sub-annual
basis. The model was evaluated on annual errors for major end-uses (heating, cooling, electricity),
both for the neighborhood together and for each building separately. The neighborhood errors for
these categories ranged between 1-19% but increased to 4-66% when looking at individual buildings
(excluding outliers).
27
Chapter 3
Proposed Methodology for Urban
Building Energy Modeling
The main steps in the creation of an urban building energy model (UBEM) consist of (1) the collection of data relevant for a thermal energy model (weather, building shapes, constructions, etc.), (2)
organization of data for input into thermal energy model (template creation), (3) execution of the
thermal simulation algorithm, and (4) validation of results by comparison to measured data. This
chapter presents a general overview of data sources and procedures that can be applied to a variety
of models. Afterwards, Chapter 4 describes a specific application of this methodology to a case study
on the City of Cambridge, Massachusetts.
3.1
3.1.1
Data Collection
Weather Data
An UBEM should attempt to use weather data specific to the location and the time period of calibration. In order of decreasing accuracy, sources of weather data include:
o Local weather data from privately-installed stations can often be accessed online through aggregators such as Weather Underground [34] or individual webpages. Data downloaded directly
from weather stations is typically recorded in sub-hourly intervals and may contain periods with
missing observations, so it needs to be post-processed into the appropriate hourly weather file
format.
o Actual Meteorological Year (AMY) files, with historical data from weather stations around
the world. This is important for calibrating or weather-normalizing a model with energy data
28
Chapter 3. Proposed Methodology for Urban Building Energy Modeling
from a specific year, since much of the energy use is correlated with the number of heating and
cooling degree days in the given year. The National Weather Service logs data for over 4,000
weather stations around the world. For locations not covered by NWS weather stations, services
such as Weather Analytics [35] provide additional AMY files by combining actual meteorological station data, U.S. National Oceanic and Atmospheric Administration (NOAA) data, and
proprietary algorithms to generate weather files for every 35 x 35-km area around the globe.
o Typical Meteorological Year (TMY) files, representing statistically "typical" yearly weather
from recent decades. These files are available for many locations around the world. [36]
3.1.2
Energy Data
Measured energy use records for groups of buildings are not readily available. Usually they need to
be obtained directly from utility companies, and such permission can be difficult to receive. Another
source of energy data is municipal databases, for those select cities that have implemented energy
disclosure laws for certain classes of buildings (see Introduction).
3.2
Building Data
Another challenge in urban modeling is collecting accurate data on building properties. Two categories
of data are needed to create a bottom-up urban model: geometric and non-geometric.
3.2.1
Geometric Properties
Envelope geometry plays a role in a building's thermal energy requirements and needs to be defined
prior to simulation. Methods that can be used for this purpose, in order of decreasing accuracy,
include:
o Combining extrusions of building footprints from 2D GIS data with LiDAR data from aerial
scans. This provides more details on the building geometries, which is especially important
when buildings have irregular shapes, roofs are not flat, or floor areas change by story. (LiDAR
has been used in solar mapping, but not yet in urban energy simulation.)
o The CityGML format for storage and exchange of city models, which specifies 3D geometries
and locations of entities [37].
o Using GIS shapefiles with building footprints, along with information on building heights or
numbers of stories, to extrude the footprints vertically to the appropriate height.
29
Chapter 3. Proposed Methodology for Urban Building Energy Modeling
o Creating one prototypical geometry for each of the building archetypes being simulated, then
scaling up energy use intensity results by floor area (as seen in [12, 13]).
Once the exterior geometry has been specified, the buildings needs to be separated into thermal
zones. Prior research work has used both single-zone and various multizone configurations. For urban
modeling purposes, multizone configurations are generally either done by floor or by splitting into
core and perimeter zones.
3.2.2
Non-Geometric Properties
The non-geometric properties of buildings can be characterized by a space of numerical or categorical
parameters, which can be separated into several types:
o Physical (fixed) parameters describe the properties of the building that remain unchanged
over time and do not depend on the occupancy. Numerical parameters can include floor area,
number of rooms, number of specific appliances, year of construction, window-to-wall ratio, and
others. Categorical ones include the presence or absence of air conditioning, type of heating
system or heating fuel, type of wall construction, and so on. While these parameters can
potentially vary when considering long time periods (e.g., windows can be upgraded or insulation
can be added), they are usually assumed to be constant over the calibration period (typically
one year).
Data sources: In-person audits, property tax assessment records, expert evaluation, local building codes, national building codes.
o Occupancy-driven (variable) parameters are ones that to a large extent are correlated with
occupancy rather than with the physical properties of the building. The number of occupants
typically determines the level of electricity use for appliances, the frequency of cooking equipment
use, and the amount of hot water used for showering, laundry, and dishwashing. Occupant
preferences and activities within the house also determine settings dependent on individual
comfort requirements, such as thermostat setpoints or the amount of lighting needed.
Data sources: Direct polling of occupants, residential surveys, national census.
o Scheduling (time series) parameters: This category is also occupancy-driven, but, while
the previous one defines steady effects of occupant number and preferences (e.g., the peak power
requirement for appliances), this one accounts for daily fluctuations using hourly schedules
(fractional, on/off, temperature).
Data sources: Appliance-level submetering, direct polling of occupants, residential or transportation surveys.
30
Chapter 3. Proposed Methodology for Urban Building Energy Modeling
Scheduling parameters become most important when calibrating to hourly energy consumption data.
Since this work looks only at calibration to monthly and annual measurements, this methodology
focuses on determining the other two types of properties: physical and occupancy-driven. The following sections present two approaches to setting these two categories of inputs. Physical properties are
assigned to the model through archetype templates, while occupancy-driven properties are inferred
from measured data and parametric analysis.
3.3
Template Generation
Because collecting all the information required for an energy model on an individual building basis
would be time-prohibitive, many urban- and national-scale modelers have used the concept of building
archetypes as a way of assigning building properties. An archetype defines a set of characteristics that
is representative of a group of buildings with similar properties.
This research proposes the use of multivariate linear regression as the first step in archetype development. Assuming some information on energy consumption is known and building properties have been
identified or estimated as described above, regression can be used to systematically select the variables by which archetypes should be distinguished. Since geometry is already accounted for within the
EnergyPlus input files, archetype templates should be based upon combinations of the non-geometric
variables that are shown to significantly affect energy use intensity. Once all combinations are defined,
any groups that contain very small numbers of buildings can be eliminated for the sake of simplicity
by merging them with more populous archetypes with similar properties.
3.4
Template Customization
After geometry has been created and archetype templates have been used to assign building properties,
energy simulation can be run. Generally, it is unlikely that the results of the energy simulation will
match measured results, even if templates are assigned correctly, due to the fact that occupants have
a greater effect on energy use than the physical properties of the construction.
Most researchers have avoided addressing this problem by validating their results in aggregate, i.e.,
summing up the energy use for an entire neighborhood/city and comparing to the measured value.
The errors for these aggregated comparisons have ranged from 4% to 21% [14]. Even in the best
cases, however, it is not likely that, if an individual building was selected from that set, it would
conform to one "typical" occupancy profile; the low error is likely a result of averaging out under- and
over-predicted spaces. Indeed, researchers that have looked at errors both on the aggregate and the
individual building scale have reported single-building errors between 5% and 99% [14].
31
Chapter 3. Proposed Methodology for Urban Building Energy Modeling
60
50
40
HEATING
30
20
10
LOAD
SBASE
1
2
3
4
5
6
7
8
9
10
11
12
6--
COOLING
4
3
2
BASE LOAD
0
1
FIGURE
2
3
4
5
6
7
8
9
10
11
12
3.1: Example of residential monthly energy use in Cambridge, MA.
This step involves taking each template as defined above and further customizing it to individual
buildings. Since the templates are assumed to be representative of constructions, this customization
step applies to settings related to internal loads and heating and air conditioning systems. For single BEMs, these settings are typically defined manually by referring to building lighting plans and
mechanical equipment schedules.
For UBEMs with hundreds of buildings, this would be infeasible.
However, if monthly measured data by building are available, certain input parameters can be inferred
in an automated fashion. The resulting distributions of these inferred parameters can then be used as
the basis for generation of models for other buildings in the same or similar neighborhoods. Moreover,
this procedure can identify inconsistencies or mistakes in the building information database being
used.
3.4.1
Inference of Parameters
Monthly energy data affords some ability to differentiate between end uses. This in turn reveals information about the buildings that could not be gleaned from annual measurements. The information
that can be inferred will vary based on the climate zone, and on whether the building uses all-electric
energy or electricity plus a heating fuel. In a climate with both heating and cooling seasons, the
32
Chapter 3. Proposed Methodology for Urban Building Energy Modeling
electricity base loads in shoulder months are typically representative of the appliance and lighting
loads in the dwelling, electric peaks in the summer indicate air conditioning, while the base fuel load
during non-heating season is primarily used for hot water (Figure 3.1). This information can be used
to customize certain parameters in energy model inputs to each building.
3.4.2
Probabilistic Estimation of Parameters
After customizing individual building templates with properties that can be inferred from the energy
data, we can go a step further and attempt to estimate those properties that are not directly deducible.
This can be done with a procedure similar to some automated calibration processes that have been
used for individual buildings.
As mentioned in Chapter 2, the parameter values resulting from a
calibration process will almost never be unique since many combinations of input vectors can provide
similar outcomes. This issue is addressed by generating probability distributions of unknown input
parameters instead of single values, using the probabilisticestimation method that has been described
by Cerezo et al. [38].
The probabilistic estimation procedure consists of the following steps:
1. Select a set of N unknown parameters (Xi).
2. Assign to each one a uniform probability distribution in the range [mini, maxi], i E [1, N], where
the minima and maxima are set based on reasonable limits.
3. Use the N uniform distributions to generate an N-dimensional grid of discrete parameter values
N
with step sizes ti. This results in a set of S =
maxi
i=1
-
ti
mini + 1) input combinations.
4. Generate and simulate a set of S files for each original (customized) building EnergyPlus input
file, calculating the calibration error for each parametric run.
5. Define an acceptable calibration error and select all results within that range. Combine the values
for parameters Xi from all those results into a multivariate joint probability mass distribution.
6. Sample the resulting probability mass distribution with Monte-Carlo methods for use in modeling other buildings in the same or analogous neighborhood.
The results of parameter inference and probabilistic estimation can all be used to create probability
mass distributions for the neighborhood being explored. If the modeled set of buildings is representative of the neighborhood, these distributions can then be used to assign properties to other buildings
for which information is not available.
33
Chapter 3. Proposed Methodology for Urban Building Energy Modeling
3.5
Model Execution
This process uses the EnergyPlus [39] simulation engine developed by the U.S. Department of Energy
(DOE). EnergyPlus is chosen for its versatility in interacting with external interfaces, ease of input
file manipulation and running batch simulations, and continuing improvements by the development
team at the DOE. Each new release of EnergyPlus is validated according to industry standards and
validation reports are available online.
3.6
Model Validation
After simulations are run, their results need to be compiled and compared to measured monthly
values. An error metric should be chosen consistent with or derived from ASHRAE Guideline 14 (see
Section 2.2.1).
34
Chapter 4
Application of Methodology
Cambridge, MA Case Study
The methodology described in the previous chapter was applied to the residential building stock in
the City of Cambridge, Massachusetts. This chapter describes the specifics of each step tailored to the
data available for Cambridge. The primary data sources were (1) monthly natural gas and electricity
readings provided by the local utility, (2) annual property tax assessments provided by the City
of Cambridge, and (3) GIS maps from the Cambridge Geographic Information System department.
Most data processing and plotting was performed using R, a free software environment for statistical
computing [40].
Analysis was limited to low-rise residential buildings with 1 to 4-family occupancy. The first step was
to merge energy data with tax assessment data by address. Tax assessor information was labeled by
parcel with a unique Map-Lot number (Figure 4.1), while energy readings were provided by account
number with a separate file linking account numbers to street addresses. After converting all addresses
to a format consistent across the two databases, each tax parcel could be associated with one or more
electric and gas meters. The rest of the analysis was conducted using Map-Lot numbers as unique
IDs.
The final subset of data contained 3,395 residential buildings across Cambridge. Of these, 453 located
in the same neighborhood, Cambridgeport, were modeled with EnergyPlus. Thus, the numerical data
analysis below was conducted on the entire sample of 3,395 buildings; the energy model results concern
just the Cambridgeport subset. The year 2008 was chosen for model calibration, as it had the largest
amount of metered energy data available.
35
Chapter 4. Application of Methodology to Cambridge, MA Case Study
FIGURE
4.1
4.1.1
4.1: Tax parcels of the City of Cambridge.
Data Collection
Weather Data
Weather data for Cambridge for the year of analysis, 2008, were obtained from two sources and
combined into one EnergyPlus Weather (EPW) file format.
Most of the data was taken from a
weather station in Central Square, Cambridge (KMACAMBR4) [41], which is located within the
area being modeled and has records dating from 2005 available. Its measurements, recorded at 5minute intervals, include drybulb temperature, dewpoint temperature, relative humidity, barometric
pressure, wind speed, and wind direction. After converting to hourly data and correcting any gaps
due to temporary sensor failure, these values were used to populate fields in a new EPW file for
Cambridge, MA in 2008. However , since the Central Square weather station's historical data did not
include solar radiation, it was supplemented by Weather Analytics' [35] AMY file for that year. This
was used to fill in values for global horizontal, direct normal, and diffuse horizontal radiation in the
new EPW file.
36
Chapter 4. Application of Methodology to Cambridge, MA Case Study
4.1.2
Energy Data
Energy data were provided by NStar (recently renamed to EverSource), the utility company servicing
parts of New Hampshire, Connecticut and Massachusetts [42]. EverSource agreed to provide MIT
with partial data on their electric and gas customers in Cambridge for calendar years 2007-2010 for
research purposes. The following steps were used to pre-process the energy data for further analysis.
1. Cleaning and merging:
(a) Energy consumption was provided with the read date for each bill. The read dates were
not consistent from building to building and typically ranged anywhere within the first or
the last week of every month, in some cases with multiple readings in one month. Since
the beginning and end dates of each bill were not consistent, they were standardized by
calendar month using the 'xts' package in R.
(b) Obvious outliers (e.g., monthly values exceeding the values of the previous and following
months by two or more times) were corrected by linear interpolation (less than 0.02% of
81,480 data points were corrected).
(c) Natural gas consumption values were converted from therms to kilowatt-hours.
(d) Since an address could be associated with one or more gas and electric accounts, all readings
for one address were summed to get the entire building use. It was not always clear whether
all of a building's accounts were included in the data provided; however, when the number
of accounts differed drastically from the number of units reported for the building and the
EUI was exceeding low, those buildings were excluded on the basis of incomplete data.
2. Selection:
(a) Only buildings which had natural gas as the heating fuel were retained, since consumption
of fuel oil or other heating fuels was unknown.
(b) The data were trimmed down to only those accounts that had at least 12 continuous months
of data.
(c) The year 2008 was chosen for model calibration, as it had the largest number of complete
observations.
After pre-processing the data and merging with building property data, energy consumption could be
normalized by floor area to energy use intensities (in kWh per square meter) for simpler comparison
between buildings. The following plots show histograms of annual EUI distributions for the building
set (Figures 4.2, 4.3) and monthly gas use and electricity use profiles (Figures 4.4).
37
Chapter 4. Application of Methodology to Cambridge, MA Case Study
-
400
I
300-
BUILTPD
E1980
02
1945
200
-
M
E
z
2014
100-
M
0
4-
0
400
200
000
800
2008 EUI (KWH/M2)
FIGURE 4.2: Histogram by total 2008 EUI, shaded by construction period (pre-1945, 1946-1980,
post-1980).
500-
400
300
S300-
-
-
-
400
-
0200
z00
200
400
000
80
2008 GAS (KWH"2)
2008 ELEC
(KWHW2)
FIGURE 4.3: Histograms by 2008 EUI, separated into gas and electric use intensities.
38
Chapter 4 . Application of Methodology to Cambridge, MA Case Study
150 -
'E 100 -
~ui
Ill
(!)
50 -
o2008-01
2009-0 1
2008-07
2009-07
Month
(A) 24 months of gas use intensity for 3,395 buildings, including gas- and oil-heated ones. The cyan color
represents buildings originally labeled in the tax assessment as oil-heated .
30 -
~~
20 -
g
Li]
10 -
0-
2008-01
2008-07
2009·01
2009-07
Month
(B) 24 months of electricity use intensity for 3,395 buildings. Peaks occur in both summer and winter months,
implying the use of both air conditioners and supplementary electric heat.
FIGURE 4.4:
Monthly energy use intensities for low-rise residential buildings in Cambridge.
39
Chapter 4. Application of Methodology to Cambridge, MA Case Study
4.2
4.2.1
Building Data
Geometric Properties
The Cambridge case study used GIS data made available by the City of Cambridge [43] in combination
with the computer-aided design software Rhinoceros 3D [44]. The exact methodology is described
below and illustrated in Figure 4.5.
1. The 3D geometry of the neighborhood was created in the Rhinoceros modeling environment
with the visual programming plug-in Grasshopper [45]. (See Figure 4.5.)
(a) A Grasshopper algorithm read in data from a GIS shapefile (SHP) and generated polygons
for the perimeter of each building in Rhinoceros.
(b) The SHP attribute table included information on the elevations of the ground and highest
point of each building; these were used to extrude the polygons to the specified height.
(c) The window-to-wall ratio was used to generate glazing surfaces distributed evenly around
the facade of a building (except on walls adjacent to other buildings).
2. The Grasshopper plug-in ArchSim [46] was used to create EnergyPlus input files (IDFs) for
each building. ArchSim processed the vertex coordinates of each surface into the corresponding
EnergyPlus surface objects with specified boundary conditions.
(a) A pre-processing algorithm was used to identify shading surfaces affecting each building.
Generating shading surfaces from every surface in the neighborhood would be computationallyprohibitive in both the IDF-generation and simulation phases, so only shading surfaces in
close proximity to each building were included.
(b) The number of stories was used to split the entire building volume into floors; each story
was represented as a separate thermal zone in the IDF files.
(c) The last step in IDF file creation was to specify building properties by assigning each one
a template defining one of the district's archetypes (see Section 4.2.2).
4.2.2
Non-Geometric Properties
For the case study, information on physical properties of buildings was provided in the form of tax
assessment records compiled annually by the City of Cambridge [47]. This is a widely-applicable
source of building information, as property tax assessments are performed in every location in the
U.S. and are publicly available, though the information recorded and frequency of updates may vary
by municipality. For each tax parcel with a building, the Cambridge tax assessment listings contained
40
Chapter 4. Application of Methodology to Cambridge, MA Case Study
.5.
(A)
2D GIS view of a section of Cambridge. Energy simulation was done for the buildings shaded green; others
contributed to shading.
(B) Grasshopper component for creation of 3D geometry and EnergyPlus input files.
(c) 3D geometry after outline extrusion.
FIGURE 4.5:
Building geretry generation process.
Chapter 4. Application of Methodology to Cambridge, MA Case Study
information such as the year of construction, number of stories and rooms, facade and roof types,
heating system types, ratings of interior and exterior conditions, and others.
Exploratory data analysis showed that sections of the tax records was either incorrect or inconsistent
with other information. This is not entirely surprising, given the fact that the role of tax assessors
does not extend beyond determining a property's value. In conversation with Clifford Cook, the
Planning Information Manager at the Cambridge Community Development Department, he called
tax assessment "a sort of black art" and acknowledged that tax records are not meant to be used for
other purposes [48]. Therefore, measured energy data was assumed to be the more reliable source
of information and was used as a check for certain tax assessment records. Some of these errors are
insignificant for the purposes of energy simulation, but others are more substantial and, if left uncorrected, can ascribe properties to a building that it does not possess, so that attempts at calibration
can result either in large errors or in unrealistic outcomes.
One example of such errors that was
noticed from combining energy data with the residential tax assessment was the field Fuel Type. It
was noticed that many of the buildings labeled as having oil fuel did not actually show a significant
difference in monthly use profiles from gas-labeled ones. These were assumed to have been either
labeled erroneously or not updated after a house had switched from oil to gas heat. Thus, Fuel Type
was re-labeled based on energy data: buildings that had near-zero gas use year-round or a base gas
load that did not rise significantly in winter were labeled as oil-heated and not retained for further
analysis, since no data was available for non-gas fuels.
The final, clean dataset contained values for 3,395 parcels in Cambridge with 24 months of monthly
electricity and gas data. Table 4.1 contains a summary of known properties of the dataset.
Property
Living Area
Bldg Value
Stories
Units
Bedrooms
Kitchens
Baths
Total Rooms
EUI 2008 Gas
EUI 2008 Elec
EUI 2008 Total
Unit
Mean
St.Dev.
Min
Max
m2
USD/m 2
kWh/M 2
kWh/M 2
kWh/M 2
226.8
1,985.2
2.3
1.5
4.1
1.5
2.5
9.3
203.0
39.2
242.2
104.8
790.4
0.4
0.7
1.6
0.7
1.016
3.255
83.9
22.8
94.5
39.0
35.2
1.0
1
1
1
1.0
2
14.8
0.5
19.3
1,183.9
8,271.8
4.0
7
13
7
7.5
28
776.8
196.2
794.6
TABLE 4.1: Summary of Cambridge residential dataset for 3,395 buildings.
42
Chapter 4. Application of Methodology to Cambridge, MA Case Study
4.3
Template Generation
The initial archetype templates were based on the results of a multivariate regression analysis relating
annual energy use intensities to building properties as predictor variables. All non-categorical building
properties were normalized by floor area, and strongly correlated variables were excluded (e.g., number
of bedrooms was correlated with number of kitchens, number of units, and total number of rooms). The
categorical variables include AC Use (No/Yes), Heating Type (Forced Air/Hot Water/Steam/Other),
Foundation (No Slab/Slab On Grade), Wall (Masonry/Non-Masonry), Roof (Flat/Sloped), Building
Type (Attached/Detached/Semi-Detached), and Built Period (Pre-1945, 1946-1980, Post-1980). All
variables were tested with annual EUIs for 2008 and 2009 to check for consistency; results are in
Table 4.2.
TABLE
4.2: Linear regression results.
Dependent variable:
2008 EUI
Intercept
Stories/sqm t
Bedrooms/sqm
Fireplaces/sqm
Building Value/sqm
Exterior Condition
AC Use
Heating: HW
Heating: Other
Heating: Steam
Foundation: Slab
Wall: Non-Masonry
Roof: Sloped
Type: Detached t
Type: Semi-Detached
Built 1946-1980
Built Post-1980
Observations
R2
Adjusted R 2
t
70.65*** (14.25)
8, 518.65*** (432.07)
1, 121.00*** (281.29)
3, 692.64*** (411.01)
0.01*** (0.003)
-9.98*** (2.01)
13.75*** (3.06)
18.18*** (3.56)
1.45 (7.18)
12.94*** (4.63)
10.41 (11.84)
-19.16*** (7.15)
15.67*** (4.73)
46.89*** (8.44)
33.35*** (8.66)
8.38 (7.15)
-56.80*** (7.91)
3,395
0.18
0.18
2009 EUI
69.53*** (14.17)
8, 571.18*** (429.78)
1, 023.02*** (279.80)
4, 189.72*** (408.83)
0.01*** (0.003)
-10.52*** (2.00)
12.59*** (3.04)
19.04*** (3.54)
1.89 (7.15)
13.09*** (4.60)
8.08 (11.78)
-16.71** (7.11)
17.01*** (4.71)
48.30*** (8.40)
32.87*** (8.62)
9.57 (7.11)
-60.92*** (7.87)
3,395
0.19
0.18
*p<0.1; **p<0.05; ***p<0.01
Note:
Some of the variables identified as statistically significant by the regression model can be attributed
purely to geometric properties (indicated with t in the table), while others need to be accounted for
43
Chapter 4. Application of Methodology to Cambridge, MA Case Study
by other non-geometric means. The geometric variables (stories per square meter, type of building) do
not need to be included in the templates since they are taken care of when IDF geometry is generated.
Among the ones not automatically accounted for by the geometry, the statistically significant ones
included:
Bedrooms/sqm: Can be interpreted as an indicator of occupancy and is positively related to EUI.
Fireplaces/sqm: The number of fireplaces seems to increase EUI significantly. This could be
due to increases in air exchange rates due to the stack effect when a fireplace is in operation,
as well as increased air leakage through a chimney with an imperfectly closed damper while a
fireplace is non-operational. It could also be partially a result of correlation between the number
of fireplaces and the building value (r = 0.41).
Building Value/sqm: Has a very slight positive correlation with EUI. This could possibly be an
indicator of a wealthier household with higher appliance use.
Exterior Condition: An integer ranking from 0 (poor) to 10 (excellent). The negative regression
coefficient implies that better exterior condition corresponds to slightly lower energy use.
A C Use: Increases the EUI when present.
Heating Type: Hot water and Steam were the categories showing significant difference from the
base case category (Forced Air).
Wall Type and Roof Type: Non-masonry walls show a negative coefficient, which could be due
to the fact that masonry buildings are not present in the most recent age group. Sloped roofs
have a positive coefficient: one possible explanation is that, if the roof is insulated worse than
the exterior walls, the sloped roofs surrounding a conditioned space would provide more surface
area for heat exchange than a flat roof.
Built Period: The results imply little difference between buildings constructed pre-1945 and
1946-1980, with newer (post-1980) buildings having lower energy consumption. This is logical
given that post-1980 construction had to comply with energy codes specifying insulation levels,
while earlier buildings had variable, if any, insulation levels.
Of the variables identified as significant above, construction period (used to define envelope constructions) and air conditioning were included in the initial template set. Masonry/non-masonry wall type
was originally included as another differentiator, but the number of buildings with masonry construction was so low that the division was deemed superfluous. The number of bedrooms/occupants and
heating type are accounted for later in the customized templates. Number of fireplaces, building value,
and exterior condition were excluded as there was no clear way to model them with EnergyPlus. The
flat/sloped roof variable was also excluded since it should in theory be defined by geometry, but with
the current GIS extrusion process roofs cannot be modeled in detail.
44
Chapter 4. Application of Methodology to Cambridge, MA Case Study
400-
WILTPEMO
-
PMu-1945
1946-1360
U)
CD
200-
0-
0
1;0
100
50
2008
20
ELEC (KWHA2)
FIGURE 4.6: Energy use shaded by age category of building.
Finally, an additional variable not used in the regression was included in the initial set of templates.
As Figure 4.6 illustrates, there is no obvious relation between gas/electric EUIs and the two older age
groups. Because of the large spread in heating (gas) EUIs, it was assumed that a more important
categorization than age would be whether (and to what extent) the house had been retrofitted with
insulation since its original construction. At the time of this study, the City of Cambridge was unable
to provide information on dates of major renovations, which would have simplified this categorization,
and no other variables in the tax assessment data could be related to presence of insulation or renovation status. Therefore, a simplified procedure based on error comparison was used to infer whether
a building had been insulated or not. All pre-1945 buildings were simulated with both insulated and
non-insulated templates. When the error results of both runs were plotted against measured EUIs,
the buildings for which the uninsulated run had better fit to measured data were assigned to the
uninsulated category. Since this categorization could not be backed up by data, its outcome will need
to be checked during validation (see Section 6.3.1).
The result of this step resulted in 8 distinct archetype templates, with divisions based on physical
parameters only: 3 age groups (pre-1945, 1946-1980, post-1980; each used to define envelope constructions typical of the time period), use of air conditioning (yes/no), and presence of insulation
45
Chapter 4. Application of Methodology to Cambridge, MA Case Study
(yes/no, for the pre-1945 set only). Occupancy-related building properties were assigned uniformly
to all templates.
4.3.1
Constructions
The built period was used to define the types of constructions used for the buildings' facades, roofs,
floors, and windows. Construction information by time period was derived from Massachusetts Building Codes [49, 50] and Architectural Graphic Standards [51]. Appendix A contains detailed tables
with types and thicknesses of the materials used in construction layers for the different templates,
along with the overall construction U-values.
4.3.2
Internal Loads and DHW
Initial internal loads were based on average of lighting, appliance and miscellaneous electric consumption for a sample of low-rise buildings in Massachusetts included in the Residential Energy
Consumption Survey (RECS) [7]. The domestic hot water load was an average from the same source.
4.3.3
Schedules
Schedules of lighting, equipment, heating/cooling setpoints and occupancy are an integral part of
any energy model and crucial when calibrating to hourly data. In this study, since measured data is
available only on a monthly basis, hourly schedules are not as crucial and were assumed to be the
same for all buildings, with just the peak loads differing. Schedules were primarily based on the NREL
publication of Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United
States [52]. Appendix A lists all the schedules used for the Cambridge model.
4.4
4.4.1
Template Customization
Inference of Parameters
Air Conditioning
The use of air conditioning can generally be detected through a peak in electric load during
summer months. In Cambridge's climate with warm but not overly hot summers (the mean
daily temperature of the hottest month is about 23 C), air conditioning is not the norm in lowrise housing. Newer constructions typically have central AC systems installed, but older homes
are usually equipped by tenants with non-permanent window ACs. These might be installed
46
Chapter 4. Application of Methodology to Cambridge, MA Case Study
in every room of the house or only in certain ones and operated based on occupant preferences
and presence in the home; their monthly electric load profiles will tend to rise in the summer
months with varying degrees.
In an EnergyPlus model of a building, cooling energy is controlled with a setpoint and daily
schedule, so it is not easy to simulate irregular use of air conditioning. It is also not possible to
simulate a cooling load in just one room when the entire floor is modeled as a single zone, which
is typical of urban models.
In order to match monthly electric loads over the cooling season by building, it is necessary to
separate them into those that have AC and those that do not. An automated way to do this
is to define a ratio of electricity in the summer months to electricity in the shoulder months
(when no extra heating or cooling is needed so the electric use is at the base load), above which
the house is considered to use air conditioning on a regular basis. In the Cambridge study, this
cutoff ratio was set to 1.5. This step, then, adds a field for ACUSE to the building data table
with the formula:
A if
ACUSE =
Esummer
Eshoul
>
-
1.5
(41)
0 otherwise
Electric Heat
Analogously to air conditioning, higher electric use in winter than in shoulder months usually
indicates use of supplementary electric heat. Since all the buildings in this study use natural
gas as the main heating fuel, it is assumed that this rise corresponds to supplementary heat
provided by space heaters. Their presence was identified using the ratio of winter electric energy
to that in the shoulder months, with 1.75 as the cutoff. It should be noted that some increase in
winter gas use over shoulder months is expected even for buildings that only use it for domestic
hot water and cooking, due to cold-weather behavior changes such as longer, warmer showers
or more frequent cooking at home.
ELECHEAT =
I if Ewar
Eshout
1.7
--
(4.2)
0 otherwise
Due to difficulties in modeling a supplementary heat source in the current setup, buildings
identified to have electric heat were excluded from further analysis.
Domestic Hot Water
Since the dataset used in the Cambridge study retained only buildings with natural gas as the
heating fuel, it was assumed that the majority of those buildings also used gas-fired tanks for
domestic hot water (DHW). While gas could also be used for cooking with gas stoves, its consumption for cooking is (1) overall lower than for domestic hot water, (2) less predictable (some
47
Chapter 4. Application of Methodology to Cambridge, MA Case Study
people might cook several meals a day at home, while others practically never use their stoves),
and (3) the number of buildings using gas and electric stoves is nearly equal [7]. Thus, this study
attributes the natural gas base load in every building to domestic hot water. EnergyPlus specifies DHW loads in terms of peak flow in cubic meters per second and a fractional use schedule,
so the natural gas energy for DHW needs to be converted to volumetric flow using:
Q = rhCpAT
V=
= pVCP(Tsup - Tn)
Q
-
(4.3)
T
(4.4)
)
pCy (TSUP - Tn
Q
= energy input rate (kW), V = volumetric flow rate (m3 /s), Tup = DHW supply
temperature (60'C), Tin = inlet (mains) temperature (13'C), Cp = specific heat (4185 J/kgK
at 37'C), p = density (993 kg/m 3 at 37'C) (liquid properties from [53]). Since energy is known
only on a monthly basis, it can be converted to the peak energy input rate once a daily schedule
has been defined:
where
EDHW month
full load hours
days x
days)(
peak
(4.5)
day
where the full load hours are the sum of all the hourly fractional values in the daily schedule.
Internal Electric Loads
Another customization that can be done is for internal electric loads. Base electric loads in the
shoulder months can be assumed to comprise the typical electricity used for domestic appliances
-1
T
anuliuiig.
LikAu
WVV,
-L
:I--
T-%TTIT
T1--
1
11
Energyrius defines
111
,.
Y
./
1)
these inputs in peak power densities (W/m-)
and fractional hourly schedules. Monthly energy values were converted to power densities after
defining fractional schedules:
Qelecload,peak =
Eelecuload
ours
day
days x
(4.6)
Since appliances and lighting are rarely separately metered in residences, it is common to lump
all internal loads together when modeling residential buildings (e.g., the ASHRAE Handbook of
Fundamentals chapter on Residential Cooling and Heating Load Calculations provides equations
for sensible and latent internal gains from occupants, lighting, and appliances combined [15];
ASHRAE Standard 90.2: Energy-Efficient Design of Low-Rise Residential Buildings also specifies a combined hourly internal heat gain profile [54]). For the Cambridge model, lighting and
appliance schedules were defined as identical since both primarily depend upon the occupants'
presence at home (with minor differences, e.g., lights are usually fully turned off at night while
some appliances, like refrigerators, stay on), and the Qelecload,peak was left as one value encompassing lighting and appliances, since end-use separation was not crucial. If more accurate
48
Chapter 4. Application of Methodology to Cambridge, MA Case Study
results are needed, it is possible to split internal electric loads into lighting and appliance components. Since appliance use is more uncertain than lighting, the lighting power density can be
set to a maximum and the rest of the internal loads assigned to appliances.
Occupancy
Occupant density can be customized to each building based on the number of bedrooms. As a
starting value, it is fair to set the number of occupants equal to the number of bedrooms (based
on RECS data for Massachusetts, the median number of occupants/bedroom is 1.0 [7]).
HVAC System Efficiency
If a list of HVAC system types is available, efficiencies can be adjusted. This is important for the
current model because the buildings are modeled with Ideal Air Loads for heating and cooling,
which implies that the simulation engine returns the demanded heating and cooling amounts
without accounting for losses in plant or distribution equipment. For this reason, efficiencies need
to be assigned post-simulation. Since this does not allow for calculation of transient efficiencies
that vary with part load, an average efficiency value needs to be used. The best such metric for
heating equipment is the annual fuel utilization efficiency (AFUE), defined as the ratio of total
annual heat output for combustion equipment to the energy of the annual fuel supply. Since
AFUE is an annual measure that accounts for transient variations, this is used as the constant
efficiency factor for heating equipment in the model.
Cooling: Since it is not possible to accurately differentiate central AC from unitary window ACs
from the data, one coefficient of performance (COP) is assigned to all buildings that were labeled
with ACUSE = 1. In this study, a COP of 2.6 was assumed as an average of federally-mandated
standards for residential room air conditioners for the years 2000-2014 [55].
Heating: The building database separated heating system types into Forced Air, Hot Water,
Steam, and Space Heat. A typical efficiency was assigned to each of these system types based on
residential heating equipment descriptions from the 2008 ASHRAE Handbook: HVAC Equipment
and Applications [56], per Table 4.3.
System
Efficiency
Forced Air
Hot Water
Steam
Space Heat
0.78
0.80
0.75
0.75
TABLE
4.3: Heating system efficiencies.
It should be noted that these values carry high uncertainty, since (1) the age of the systems is
unknown, (2) distribution losses can vary based on location and condition of piping or ductwork,
49
Chapter 4. Application of Methodology to Cambridge, MA Case Study
(3) the system type might not be assigned correctly in the tax assessment database in the first
place.
4.4.2
Probabilistic Estimation of Parameters
The probabilistic estimation procedure was conducted as described in Section 3.4.2 focusing on nondeterministic parameters resulting from occupant behavior and preferences. Specifically, the ones analyzed here were occupant density (OCC) and heating and cooling setpoints (HEATSET, COOLSET).
The ranges and step sizes defined for the parametric analysis are listed in Figure 4.4.
Parameter
Unit
Min
Max
Step Size
OCC
HEATSET
COOLSET
people/m 2
C
0
C
1/unit*
18
22
2/bedroom*
24
28
Variable (6 steps)*
2
2
*Occupancy ranges are not uniform but depend on units and bedrooms per building.
TABLE
4.5
4.4: Parametric analysis settings.
Model Iterations
Using the steps of progressive template customization described above, four iterations of the energy
uuined ini Tabc 4p.t. Te
. T
are
moue wIre simuiated, suarting frm least to m.st pc
results of these are reported in the following chapter.
Run , Templ
Divisions
Elec Load
2
(W/m )
0
1
2
3
1
8
8
8
N/A
Age/Insul/AC
Age/Insul/AC
Age/Insul/AC
DHW
(m3/s/m2)
Heat Eff
(%)
0.85
4.5 * 10-8
13.3
4.5 * 10-8
0.85
13.3
Inferred from building & energy data
Inferred from building & energy data
TABLE
4.5: Energy model iterations.
50
Occup
Setpoints
2
(pp/m )
(H/C)
0.021
0.021
20 0 C / 25-C
20 0 C / 250 C
20 0 C / 250 C
Param/Param
Param
Chapter 5
Results
Cambridge, MA Case Study
This chapter presents results for the simulation of 453 buildings in Cambridgeport. Figure 5.1 shows
the 3D model for this subset of Cambridge. The buildings in the chosen neighborhood that were not
being simulated due to lack of energy data for calibration were still included in the 3D file for use as
shading surfaces.
FIGURE
5.1
5.1: Rendering of the Cambridgeport 3D model.
Error Metrics
Several error measures were considered for application in the urban context. The chosen metrics
to compare results of different model iterations are shown below. The Relative Error (RelErr) is
51
Chapter 5. Results for Cambridge, MA Case Study
a reflection of the overall annual error in energy use intensity (EUI), while the Goodness of Fit
(GOF) accounts for both the error in annual means (through NMBE) and monthly variances (through
CVRMSE) (definitions in Section 2.2.1).
RelErr
GOF
/
WC
-
Emeas,yr - Esim,yr
Emeas,yr
CVRMSE
VRMSECM2
+
(5.1)
NMBENMBE
2
(5.2)
"CVRMSE +WMBE
Here,
wCVRMSE
equal to
and
WNMBE
are weighting factors such that
= 0.25 and
WCVRMSE
+
WNMBE =
1. They were set
= 0.75. The CVRMSE and NMBE were calculated based on
the monthly total energy use (sum of electricity and gas). An attempt to calculate these statistics for
monthly electric and gas use separately and then combine into one NMBEGOF metric resulted in low
measured electricity values being too influential. Therefore, to reflect the dominance of natural gas
energy over electric, both NMBE and CVRMSE were calculated based on monthly energy sums.
5.2
WCVRMSE
WNMBE
Annual and Monthly Simulation Results
The plots below illustrate the annual and monthly results from each simulation run. The runs were
set up as described in Section 4.5 and summarized below:
Single Template
Run 0
Baseline run with a single template for all buildings.
Multiple Templates
Run 1 Buildings assigned to 8 initial templates, separated into 3 age groups (pre-1945, 1946-1980,
post-1980), AC (yes/no), and insulation (yes/no, for the pre-1945 set only).
Run 2 Same templates as above, but each building is customized for occupancy, DHW, electric
loads, and heating system efficiency.
Multiple Templates + ParametricAnalysis
Run 3 Uses customized input files from RUN 2 to conduct parametric runs with varying occupancy
density and heating/cooling setpoints. Results presented are for lowest-error runs for each
building.
52
Chapter 5. Results for Cambridge, MA Case Study
RUN 0: Single Template
This simulation assigns a single template (1946-1980 age group with AC; template details in Appendix A) to all buildings to be used as a baseline for assessing the next iterations. The plots below
show measured versus simulated annual energy use intensity (in order of increasing measured EUI),
the Goodness of Fit for each building (calculated as shown in Section 5.1) that accounts for monthly
agreement between values, and the Relative Error, which represents just the difference in annual energy use. (Note that the vertical axes are cut off so that the largest errors are not displayed in the
plots.)
The results for this run show annual energy consumption being under-predicted for the majority of
buildings, indicating that the constructions defined in the template are likely better than actual.
Additionally, the simulated buildings do not show much variance in EUI compared to the variance
exhibited by measured data. Finally, it is seen that very few buildings have annual errors under 10%.
700
-Measured
4Simulated
600
Sb)
400
300
200
100
0
200
Igo
160
40
Z 20
104)
90
60
40
20
00%
40%
-0%
0%
F20%nl
40%
-60%
-100%
FIGURE 5.2: Annual results for baseline run with a single template assigned to all buildings.
53
Chapter 5. Results for Cambridge, MA Case Study
RUN 1: Initial Templates, Uncustomized
This run separated buildings into 8 templates, categorized by 3 age groups (pre-1945 (insulated), pre1945 (uninsulated), 1946-1980, post-1980) and use of air conditioning (details in Appendix A). This
separation results in greater variation in the simulated EUIs and closer agreement to measured data,
which demonstrates the large effect insulated/uninsulated facades can have on energy use. However,
buildings with the smallest and largest measured EUIs are not well matched by the simulation, showing
that constructions and AC are not enough to account for all the variation in energy use.
700
Simulated
-Measured
600
200
50
200
140
W
50
140
120
160
00%
-0
0
I
FIGURE
5.3: Annual results for Run 1 with initial templates generated from annual data.
54
Chapter 5. Results for Cambridge, MA Case Study
RUN 2: Multiple Templates, Customized By Building
This was done
In this simulation, each of the 8 templates was customized to individual buildings.
could be inferred from
by changing values in the EnergyPlus input files based on parameters that
of extremes
monthly energy data (Section 4.4.1 details this process). This results in better matching
at both ends of the spectrum and reduces the magnitudes of relative errors.
704)
"Simulated
-Measured
600
500
400
200
180
164
140
110
100
80
60
40
20
60%
f6 20%
420%
-60%
-80%
101"1
on parameters inferred
FIGURE 5.4: Annual results for templates customized to each building based
from monthly data.
55
Chapter 5. Results for Cambridge, MA Case Study
RUN 3: Parametric Analysis for Customized Templates
This final run takes the customized templates from Run 2 and further adjusts them to occupancy
parameters by varying the heating setpoints, cooling setpoints (for buildings with AC), and occupant
density. All combinations of these three parameters are simulated and the runs with the smallest error
(GOF) by building are selected. In some cases, multiple combinations of parameters can result in the
same smallest error; in that case, just one of these was chosen for this part of the results, but all such
low-error combinations are included later when generating parameter distributions. (Note that the
number of buildings simulated in this run is smaller than in the previous three-404 rather than 453;
this is due to the fact that buildings inferred to have supplementary electric heating in winter were
excluded prior to simulation.)
This plot provides the closest match between measured and simulated consumption. Buildings with
EUIs at the low end are still not explained but, overall, the annual relative error and goodness of fit
are closer to zero much more often than in previous runs.
m*Simulated
-Measured
600
50
160
40
-40"
0I 4
V
FIGURE
V
N
10%rpq
5.5: Annual results for lowest-error parametric runs with variable occupancy and setpoints.
56
.......
....
....
......
Chapter 5. Results for Cambridge, MA Case Study
5.2.1
Monthly Comparison
Figure 5.7 shows the iterative improvement in the matching of monthly results to measured ones
shown in Figure 5.6. Energy bills show that, while the monthly gas use profile keeps a consistent
shape for all buildings, the electricity one has much more variation relative to its mean due to its
lower weather dependence and higher occupancy influence. A subset of 200 buildings out of the 453
simulated are plotted. The first two runs, with uniform electric and domestic hot water loads across
the building stock, show little in common with the measured data profiles since between-building
variation in base loads is not modeled. Plots for Run 2 with customized templates much better cover
the variation in base loads that is seen in measured data. Run 3 shows the same buildings adjusted to
better match measurements-electricity use is lowered where necessary by increasing cooling setpoint,
and gas use changed using occupant density and heating setpoints. Note that the gas use plot for this
run matches the measured one more closely than Run 2, since lines of the same shade correspond to
the same building.
2
FIGURE
3
4
6
7
9
W3
11
12
5.6: Measured mnonthly gas and electric use intensity.
57
.....
.....
-
..
...
..
......
.-
Chapter 5. Results for Cambridge, MA Case Study
40
40
35
.89
25
25
30
2
4
5
6
0
7
9
10
11
2
1
12
12
10
10
8
8
6
6
2
2
I
0
2
4
3
8
1
8
7
9
10
11
5
6
5
6
8
9
10
11
12
8
9
0
II
12
0-
0
12
4
2
1
(A) Run 0 (single template) results.
2
3
4
7
(B) Run 1 (initial templates) results.
50
50
45
4
40
40
1
30
2
N
_0
10
10
0
1
2
4
3
5
6
7
8
9
10
11
0
12
1
2
3
I
2
3
4
5
6
5
0
7
8
10
9
11
2
12
12
25
25
88
6
6
4
4
2
2
0
1
2
3
4
0
5
6
7
8
9
10
11
12
(c) Run 2 (customized templates) results.
4
7
8
9
10
11
12
(D) Run 3 (parametric simulation) results.
FIGURE 5.7: Monthly simulation results for a subset of 200 buildings.
58
. . ............
....
......
Chapter 5. Results for Cambridge , MA Case Study
5.2.2
EUI Distribution Comparison
Another way to visually evaluate the results of each run is by comparing the EUI histograms for
the neighborhood modeled. Figure 5.8 does this with density plots of simulated EUis along with the
measured EUls. In the first run, simulated EUis are closely concentrated around one peak value, since
the only difference between buildings is their geometry. As the number of templates increases, the
EUI variance increases as well. In Run 3, the simulated EUis have comparable variance to measured,
although the frequency of low-EUI buildings is still slightly higher than in reality.
:;~
200
200
• Measured
150
.,
Simulnred
Simulated
150
~
~
~
~
[
·'~~·
.... 100
50
0
-
50
I
100
~
[
j·
...
~~1
.... 100
IfIII • --
so
I
150
200
250
JOO
350
400
450
500
550
650
600
700
0
so
100
150
200
EUI (kWb/m2J
250
300
350
400
450
500
550
600
650
700
EUI (lcWblm2)
160
160
• Measured
Simulalcd
• Measured
140
140
120
120
100
100
80
i
11111
60
60
40
40
20
20
~
~
l
• Measured
l,.;
0
50
100
ISO
200
250
300
350
400
450
500
550
6oo
650
o -0
so
700
100
150
200
250
300
350
400
450
500
550
Simulated
-
600
650
-- -
EUl (k Wh/m2)
EUI (kWb/m2)
5.8: Measured (shaded grey) and simulated (shaded green) EUI distributions for neighborhood, in W /m 2 : Run 0 (top left) , Run 1 (top right) , Run 2 (bottom left) , Run 3 (bottom right) .
FIGURE
59
700
Chapter 5. Results for Cambridge, MA Case Study
5.3
5.3.1
Parameter Distributions
Inferred Parameters
The plots below demonstrate the distributions that resulted from parameter inference described in
Section 4.4.1. Monthly energy data for each building was used to deterministically estimate the values
for internal electric loads (appliances and lighting combined), domestic hot water flow rates, and the
presence or absence of air conditioning in every building. Additionally, building tax assessment data
was used to estimate the efficiency of the heating system. If the sample of buildings from which
these distributions were derived is representative of the larger environment, they can be sampled for
assigning parameter values to buildings with unavailable information.
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.02
0.00
0
2
4 6
8
10
12 14 16 18 20 22 24 26 28 30 32 34 36
Peak W/m2
FIGURE 5.9: Distribution of peak electric load intensities.
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Peak m3/a/m2
Z1OA(-8)
FIGURE
5.10: Distribution of peak domestic hot water flow.
60
Chapter 5. Results for Cambridge, MA Case Study
0.70
0.70
0.60
0.60
0.50
0.50
0.40
0.40
0.30
0.30
0.20
0.20
0.10
0.10
0.00
0.00
0
FIGURE
5.3.2
r
1
0C
C0
5.11: Distributions of AC use (left) and heating system efficiencies (right).
Probabilistically-Estimated Parameters
Following the procedure described in Section 3.4.2 for the probabilistic estimation of the three chosen
parameters (occupancy density, heating and cooling setpoints), a batch of parametric runs was simulated for every building. The cutoff error below which the results of a parametric run were considered
acceptable was set equal to a Goodness Of Fit of 10%. This cutoff resulted in 49% of buildings having
one or more acceptable parametric runs. (A higher error cutoff of GOF = 15% resulted in 64% of
buildings acceptable; raising the cutoff to 30% resulted in 84% of buildings having results within the
limit.) The frequency of combinations of the 3-component vector of (OCC, COOLSET, HEATSET)
10% cutoff, the
parameters were compiled into joint probability mass distributions. For the GOF
distributions are illustrated in Figures 5.12 and 5.13.
0.070
0.060
0.050
0.040
0.030
0.020
0.0 10
0.000
S~
000
SfC'
F,"C. ;4 I.Q
--
0NNW
FIGURE 5.12: Distribution of occupancy density per square meter for GOF < 10.
The occupancy distribution has a wide spread, which seems reasonable considering the large spread
in EUIs as well as in the average room size (inverse of the number of rooms per unit floor area)
of the sampled buildings. The heating setpoint distribution is maximized at 20 to 22'C as can be
expected. The cooling setpoint distribution increases with setpoint, with a setpoint of 28'C occurring
61
Chapter 5. Results for Cambridge, MA Case Study
0.30
0.30
0.25
0.25
0.20.
0.20
0.15
0.15
0.10
0.10
0.00
0.00
22 24 26
28
0
18
20
22
24
FIGURE 5.13: Cooling (left) and heating (right) setpoint distributions for GOF < 10.
most often. While this is too high be a typical cooling setpoint, it can be explained by the fact that,
among the buildings simulated as having cooling, many do not have their air conditioner turned on
constantly. Thus, since cooling schedules were not varied in the simulations, the higher setpoint can
be treated as a proxy for reduced hours of operation.
These distributions represent a probabilistic method for the characterization of occupancy-related
parameters in urban archetypes using prior information (uniformly-distributed parameter ranges)
with measured monthly energy data. These distributions can be sampled with Monte Carlo methods
to produce large numbers of possible input combinations. Each of these samples can then be used in
a simulation, resulting in a set of simulation results that demonstrates the probabilities of occurrence
of different outcomes given the parameter uncertainties.
5.4
Results Summary
Error measures on a per-building and aggregate basis are summarized below. The monthly calibration,
represented by the Goodness of Fit metric, improves with each step while its variance decreases. It
is interesting to note that between Runs 2 and 3, the mean of the annual Relative Error does not
change, but the GOF improves dramatically (from 31.6% to 18.4%).
Furthermore, it is seen that
the aggregated relative error (i.e., the error in the EUI based on the sums of the energy and square
footage for all buildings) is not a very good indication of the fit of the model. The aggregated error
is almost the same for Runs 1 and 3, and lower for Run 2, with all under 6%; yet, this metric does
not convey the spread in EUIs that was seen in Figure 5.8.
In all of the runs, the absolute values of maximum errors are high-in the 500% range for Run 0, down
to 300% for Run 3. This is primarily due to the buildings with unusually low energy use intensities,
for which even differences small in magnitude result in large errors relative to the low measured EUI.
As Figure 5.14 shows, most GOF values are concentrated under 50% for all four runs, and in Run
3 the majority does not exceed 20%.
During the validation stage, buildings with errors outside 3
standard deviations of the mean error should be inspected to identify potential explanations.
62
.............
Chapter 5. Results for Cambridge, MA Case Study
Aggregated
By Building
Run
0
1
2
3
TABLE
Annual
Monthly
Annual
RelError
NMBE
CVRMSE
GOF
RelError
Mean
StDev
Min
Max
3.5
57
-464
75
3.9
61.8
-505.9
82.3
57.1
45.1
13.4
549.7
45.6
43.5
4.6
510.5
16.5
Mean
StDev
Min
Max
-12.7
49.7
-457.6
77.2
-0.7
56.0
-499.2
246.8
48.7
42.2
14.0
505.3
36.7
43.4
4.8
499.8
Mean
StDev
Min
Max
-8.3
36.4
-309.3
46.4
-9.1
39.8
-337.4
50.7
45.1
31.7
15.5
382.3
31.6
28.4
5.2
342.2
Mean
StDev
Min
Max
-8.3
24.0
-244.3
40.7
-9.1
26.1
-266.5
44.4
34.0
27.0
13.1
318.3
18.4
23.2
4.2
272.1
Stat
(EL -35.6)
(NG 26.5)
-5.5
(EL -18.9)
(NG -2.8)
-4.2
(EL -13.8)
(NG -2.3)
-5.4
(EL 8.2)
(NG -7.7)
level.
5.1: Summary of validation results by run, time period considered, and aggregation
100-
00
50-
Wa
I
RLNOGOF
RUNI-GOP
RLW-GOF
RUN3GC0F
Run
that the vertical axis is
FIGURE 5.14: Goodness of fit by individual building for every run. (Note
displayed.)
not
are
points
error
highest
the
truncated, so
63
.
.
..
..........
Chapter 6
Discussion and Conclusion
6.1
Discussion
The goal of this work was to develop a method for iterative refinement of an urban energy model given
information on a set of buildings and the monthly energy measurements. The method consisted of (1)
generation of a set or archetype templates after identifying variables with significant effect on energy
use, (2) automated customization of templates for individual buildings based on parameters inferred
from energy data, and (3) probabilistic estimation of other unknown parameters through parametric
analysis. The results showed a clear improvement in agreement with measured data with each step.
Furthermore, the customization and parametric analysis steps enabled the creation of probability mass
distributions for a set of parameters representative of the chosen neighborhood. The contributions of
this research are summarized below.
6.1.1
Parameter Uncertainty Reduction
In the practice of urban modeling, occupancy-driven parameters have so far typically been defined
as uniform across a building archetype, due to lack of definite information and their unpredictability. When validating a model on a spatially-aggregated, annual basis, these parameters cannot be
identified and checked for accuracy. However, in order to make urban models that would be representative of reality on smaller spatial and time scales, matching end-use energy consumption at hourly,
daily, or monthly intervals becomes important. This work used monthly energy readings for a set of
buildings to generate distributions of such difficult-to-predict occupancy-driven parameters, both by
direct inference and by probabilistic analysis. The results included distributions for internal electric
load density, domestic hot water use, occupant density, and heating and cooling thermostat setpoints.
64
Chapter 6. Discussion and Conclusion
These distributions are a result of applying measured energy data to prior uncertainty intervals, specified as uniform distributions between a given minimum and maximum. The resulting distributions
are more informative and useful than either the uniform distributions or the single values typically
assumed in urban modeling.
This probabilistic method proposes an alternative to the typical most-probable-outcome single simulation result by allowing for the generation of sets of simulation results that account for parameter
uncertainty. This can be done with Monte Carlo analysis, in which the probability distributions for
multiple variables are sampled to produce hundreds of possible input combinations. Each of these
samples is then used in a simulation, and the set of simulation results demonstrates the probabilities of different outcomes subject to parameter probabilities. Having a set of probabilistic outcomes,
rather than a single one, in turn allows greater understanding of uncertainty when using the building
models to evaluate results of interventions in the building stock. This is of value to decision-makers
or financing entities when assessing possible retrofit policies.
6.1.2
Generation of Improved Archetypes
Using monthly data for calibration allowed the customization of initial building archetypes with
occupancy-related parameters defined in the form of probability distributions. Thus, the outcome
of this analysis can be translated into a set of archetype templates defining fixed building properties,
along with a set of distributions defining parameters with less certainty and greater variability. These
templates and the accompanying distributions can then be considered to comprise a full description
of buildings in a given neighborhood, with a mix of deterministic and probabilistic parameters. These
descriptions can be used to generate model inputs for buildings in a similar neighborhood for which
energy data is unavailable.
6.1.3
Evaluating Consequences of Data Availability
As Aksoezen et al. [22] noted, access to measured energy data can greatly improve generation and
characterization of building archetypes for urban modeling. Aksoezen was primarily referring to
annual data since it is most often available; this study looked at whether using monthly data instead
of annual would provide even more of an advantage in archetype definition. Annual data was still
used for the multivariate regression to identify initial archetype templates; monthly data was used
after that to further customize the templates to every building.
This work is the first detailed look at monthly, individual-building results for a bottom-up urban
model. It has demonstrated that the same relative error in annual EUI calculated for a district in
aggregate-the quantity used most often for validating urban models-can result from models with
65
Chapter 6. Discussion and Conclusion
very different input parameters and monthly energy uses (e.g., Runs 1 and 3). Thus, the usual urban
model validation metric is not sufficient to ensure a model representative of actual conditions.
In addition to disaggregated energy data, it is essential to note the importance of accurate building
property data. Many of the building properties used in this analysis were taken as givens from the tax
assessment database; however, those properties that could be checked using energy data (e.g., heating
fuel, presence of air conditioning) showed many inaccuracies. In addition, the property database had
inconsistencies among its own data fields (e.g., the number of kitchens listed as very different from the
number of apartment units, or the number of stories being different from what a recent photograph
of the building shows). This throws doubt on the fact that other properties were all documented
correctly, and puts into question the results of analysis based on these properties. In this study,
many mistakes in the most important fields (such as floor area or number of stories) were corrected
manually by consulting an online property database. In most cases, however, it is impossible to check
every piece of information when modeling hundreds of buildings, so consistent and accurate data on
building characteristics would be highly beneficial for increased accuracy in urban-scale models in the
future.
6.2
6.2.1
Limitations
Geometric Limitations
One limitation of the current workflow is that sloped roofs are not modeled explicitly due to lack of
infnrmationn nn the rnnf shapes, even thoughcr
thke regcrei::Qin mnAde1 in
Chapteor A ideantfiedar
rnnf
shaper
to be a significant predictor of building EUIs for this dataset. This information could be gathered
from LiDAR imaging of cities. In the case of Cambridge, LiDAR scans exist at least for some areas of
the city and have been used to create true 3D models of portions of the city with more accurate roof
shapes (Figure 6.1). However, only certain neighborhoods have so far been modeled using LiDAR,
and most of the 453 buildings in this case study did not have this information available. Therefore,
the 2.5-D model with flat roofs was kept for this study. In the future, a process to incorporate LiDAR
data using the same Grasshopper component should be developed.
Another aspect that was not taken into account in the current model was basements. At this stage,
all buildings were modeled as having slab-on-grade floors with earth contact, because information on
basements was not part of the tax assessment dataset. When this information is available, basements
should be modeled with the appropriate constructions and internal loads, which can be inferred based
on whether a basement is finished or unfinished. Finally, all buildings were modeled with the same
window-to-wall ratio (0.15), considered representative of this building stock.
66
Chapter 6. Discussion and Conclusion
(A)
(B) Detailed roof geometry using LiDAR scans.
3D geometry used in model.
FIGURE 6.1: 3D models of Cambridgeport with and without LiDAR data.
6.2.2
Modeling Simplifications
Several simplifications were incorporated in the EnergyPlus models in order to enable automated
model generation and quick simulation at the urban scale. First, each floor was modeled as a single
thermal zone, which precludes precise representation of the heat transfer that occurs between rooms
of variable occupancy and time of use. However, since the arrangement of rooms by floor is unknown,
this simplification is unavoidable.
Second, heating, ventilation and cooling systems were not explicitly modeled in EnergyPlus with
central or unitary equipment. Instead, constant efficiency factors were applied post-simulation to
the energy demand for cooling and heating; fan and pump electric use, if present, was not explicitly
modeled. (That contribution was automatically included in the measured electric load to which the
modeled appliance base loads were customized; however, this does not account for seasonal variations
in fan and pump loads).
Another simplification was the specification of uniform schedules across the building stock. Besides
neglecting hourly between-building or between-floor variations in behavior, this also neglects any
seasonal variation, such as from students leaving their apartments empty over summer or winter
vacations.
6.2.3
Limitations of Results
While the final iteration of the model showed good agreement with measured data (mean monthly
Goodness of Fit under 20%, mean annual error under 10%) and appropriate separation into electric
and natural gas consumption ( 8% annual error each, when summed over all buildings), it still does
not ensure that further breakdowns by end-use within each type are accurate. If more precise results
67
Chapter 6. Discussion and Conclusion
are needed, more input data will need to be provided to the model, such as counts of appliances and
light fixtures, operating hours of each, and so on.
Furthermore, although the variables chosen for parametric analysis (occupant density and setpoints)
improved the model's Goodness of Fit significantly, there is no guarantee that they were actually the
ones responsible for the metered energy variation. As in any calibration problem, multiple solutions to
matching measured data are possible; the resulting distributions provide just a subset of such solutions
that involve only those three variables. We believe the resulting distributions are useful due to the
reportedly large influence of occupants and setpoints on a home's energy use; however, this assumes
that the rest of the building's parameters-primarily the physical ones-were specified accurately,
which is not necessarily the case. This limitation is addressed further in the Future Work section.
6.3
6.3.1
Future Work
Validation
While the proposed methodology resulted in good agreement between simulated and measured energy
use, the energy models were dependent on assumptions that were made during template customization.
These assumptions (e.g., use of air conditioning or electric heating, presence of insulation) were inferred
from energy data but not yet verified against reality. The next step in validating this methodology
would be to collect more information on the actual buildings that were modeled and check whether
the assumptions made in template assignment and customization were indeed correct. Since the City
of Cambridge does not maintain such records, this validation could he done by either in-nperson viq1sal
inspection of the buildings to identify characteristics visible from the outside or through surveys sent
to building occupants. The latter method would be preferred since occupants have better knowledge
of their home (e.g., whether it has been renovated or how often air conditioning is used); these
surveys should be designed to be short and simple to complete. Validation of this methodology will
be performed in the upcoming months.
Another type of validation concerns the application of the resulting parameter distributions to modeling buildings that were not part of the original dataset. A new batch of IDF files will be generated
for another subset of residential buildings in Cambridge, with Monte Carlo sampling from the distributions used to specify parameters. Since energy data is available for almost 3,000 more buildings in
addition to the ones used in the model of Cambridgeport, these can be used as the testing set for the
archetypes and distributions generated from the model.
68
I
Chapter 6. Discussion and Conclusion
6.3.2
6.3.2.1
Methodology Refinement
Sensitivity Analysis
The proposed methodology focused on probabilistic estimation of occupancy-related parameters, but is
flexible enough to be used for almost any of the thousands of inputs to an EnergyPlus model. As a way
to improve on the methodology and justify the set of parameters chosen, extensive sensitivity analysis
should be performed. Sensitivity analysis has been frequently used as the first step in calibrating a
building energy model in order to separate parameters that are insignificant and can be fixed and from
more influential ones that should be varied in the calibration process. This sensitivity analysis has not
yet been done on an urban scale, but would provide further insight into the proposed methodology.
6.3.2.2
Hourly Energy Data
A further step will be to use the proposed methodology with hourly measured data. This will add
several challenges, including logistical (much larger volumes of data, requiring higher computing power
for processing) and methodological (choice of parameters and level of detail for calibration). Hourly
data should primarily enable the determination of daily schedules, which at the level of this study
were fixed. Distributions of fractional energy use at each hour of the day derived from measured
data would allow the creation of better daily profiles for urban models. This would be most valuable
for utilities trying to forecast power demand for new or changing neighborhoods, and would be an
improvement over current urban models that generate hourly results based on "standard" occupancy
and appliance use profiles.
6.3.3
Automation
For this case study, the processes of generating templates and customizing them were partially automated but still often required manual intervention. Initial templates and IDF files were generated
in Grasshopper with ArchSim. Afterward, Python scripts were used for customization of EnergyPlus
IDF files, initialization of EnergyPlus simulations, extraction of results from EnergyPlus output files,
and calculation of calibration errors from these results. GenOpt [57] was used to generate and run the
parametric input files for each original IDF. Finally, Microsoft Excel was used to compile results from
all runs and generate the plots in Chapter 5. In order to speed up the process and simplify dealing
with larger datasets, it would be beneficial to centralize and automate the workflow and interactions
between the different software tools to a greater extent. Additionally, the setup of a better data
management system and cloud-based simulations should be explored.
69
Chapter 6. Discussion and Conclusion
6.4
Conclusion
The methodology presented in this work uses publicly available building data and information provided by the utility company to characterize the low-rise residential building stock in Cambridge,
Massachusetts. It results in an improvement of 27% over modeling all buildings with just one template based on building function, and 18% over the best urban modeling practice of defining archetypes
based on annual energy use data. This methodology provides a way to generate a set of templates
plus probability distributions for modeling buildings within the same stock that is expected to be
useful for modeling buildings for which data is not readily available. The proposed methodology will
be validated on a test set of other residences in Cambridge, and results of the validation are expected
to guide further research on this subject.
A side goal of this work was to identify gaps in current urban building energy modeling and validation
practices and advocate for greater transparency in building energy data sharing. Accurate data on
energy use and building thermal properties are essential for developing more reliable representations
of building archetypes, and are the only resource to increase confidence in the predictive value of
district energy models for use in sustainable urban planning.
70
Appendix A
Energy Model Templates
A.1
Constructions
Pre-1945, Insulated
Category
Pre-1945, Uninsulated
Exterior Wall
Oriented strand board
Air layer
Gypsum board
U-value
0.022
0.1
0.019
0.97
Oriented strand board
Cellulose fiber insulation
Gypsum board
U-value
0.022
0.1
0.019
0.36
Roof
Asphalt shingles
Plywood wood panels
Gypsum board
0.013
0.02
0.013
U-value
1.89
Asphalt shingles
Plywood wood panels
Fiberglass batt
Gypsum board
U-value
0.013
0.02
0.08
0.013
0.42
Concrete
0.15
U-value
4.17
Concrete
Extruded polystyrene
U-value
0.15
0.025
0.93
Ground Floor
Fenestration
Infiltration
Double-pane, low-e, clear
0.6 ACH
Single-pane, clear
0.8 ACH
71
Appendix A. Building Archetype Templates
Category
1946-1980
Exterior Wall
Oriented strand board
Fiberglass batt
Air layer
Gypsum board
0.022
0.05
0.05
0.019
U-value
0.54
Asphalt shingles
Plywood wood panels
Fiberglass batt
Gypsum board
0.013
0.02
0.1
0.013
U-value
Ground Floor
Concrete
Extruded polystyrene
U-value
Fenestration
Infiltration
Double-pane, clear
0.6 ACH
Roof
Post-1980
Oriented strand board
Extruded polystyrene
Hardwood
Fiberglass batt
Gypsum board
U-value
0.016
0.05
0.04
0.05
0.019
0.30
0.35
Asphalt shingles
Plywood wood panels
Fiberglass batt
Extruded polystyrene
Gypsum board
U-value
0.013
0.02
0.152
0.05
0.013
0.17
0.15
0.025
0.93
Concrete
Extruded polystyrene
U-value
0.15
0.05
0.52
Double-pane, low-e, clear
0.5 ACH
72
Appendix A. Building Archetype Templates
A.2
Schedules
Schedule
Weekend
Weekday
0i9
0 0.9
09 9
A a9
0,
09
7
0
IiiiI
04
0-20Q2 02
022 02 11 220 02
Occupancy
123 4 5
ij
n,,rns
1at
i Ciii
Light, Equip
0.,05
91
0-9
iiiIII
Q2
a2
0203
92
0.8
,2
2
U
Ii 02
04
.0
02
03
C7
7
2
a
0
A
W
t
2s
07
CA Q.
1,A
00.9
9
1
000
000o1
1234347911121misihi is
Hot Water
73
19toi:2
II232
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