Environmental Performance Simulation
From evaluating performance to suggesting new forms
for buildings and neighborhoods
DIVA
Christoph Reinhart
Simulation Games
Urban Scorecard
Barcelona Global Energy Challenges
Energy in Buildings and Cities – June 13 2013
Climate Change and Finance
Cambridge Solar Map
Massachusetts Institute of Technology
Department of Architecture
Building Technology Program
Sustainable Design Lab @ MIT
Carlos Cerezo
Karthik Dondeti
Timur Dogan
Diego Ibarra (GSD)
Kristian Fennessy
Alstan Jakubiec
Nathaniel Jones
Christoph Reinhart
Krista Palen
John Sullivan
Tarek Rakha
Zahraa Saiyed
“Our research goal is to change current sustainable design practice by
developing, validating and testing workflows and metrics that lead to
improved design solutions as far as occupant comfort and health as well
as building energy use are concerned. The premise of this work is that an
informed decision is a better decision.”
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Our work has been supported by
Natural Resources
Canada
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Solar Buildings
1978
Two-storey sunspace
Thermal mass
Active air circulation
83% solar heated
Balcomb Residence, Santa Fe, NW (around 1978)
Photo taken from Lechner, Sun Wind and Light
Green Buildings
1991
South-exposed half cylinder
Air-to-ground heat exchanger
Ventilation-heat-recovery
Solar Hot Water
Photovoltaics 4.2kWpeak
Transparent insulating panels
Fuel cells and hydrogen tanks
Self-sufficient solar house Freiburg, 1991
Architecture: Planerwerkstatt Hölken & Berghoff
3
Green Buildings
2007
‘Made in Germany’, triple
glazing, Phase Change
Materials, exterior wooden
shutters, PV on the roof,
integrated lighting system,
clear architectural forms
Solar Decathlon Winner 2007 (Team Germany)
Photo taken from flckr.com
Basic Hypothesis
Saving energy in the building sector has largely
become an issue of system integration and
information sharing.
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Building Performance Simulation
Building Performance Simulation: An computer-based attempt to model
the various energy and mass flows within a building in order to predict
one or several performance aspects of a design.
Operational Energy
Return on Investment
Solar Radiation
Occupant Comfort
Daylight
Walkability
Today’s Presentation
From evaluating performance to
suggesting new forms
 Environmental performance simulations are now capable of predicting
the physical performance of commonly used building typologies and
technologies.
 Parts of this analysis can be done by architects and planners
themselves. Is this true/desirable?
 How can we make sure that designers are using the tools accurately
and effectively?
 We are now moving toward whole city energy models in which
different energy flows are monitored are optimized.
5
Why Environmental Performance Simulation?
 To demonstrate code compliance and to reduce risk.
 To compare different design variants.
Do today’s simulation programs
work?
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ASHRAE thinks so.
Purpose of the Standard
 Analyzing and diagnosing building
energy
simulation
software
using
software-to-software and software-toanalytical-solution comparisons.
 Checking a program against a previous
version of itself after internal code
modifications such as an algorithmic
change.
ASHRAE 140 -2007 Standard
Method for the Evaluation of
Building Energy Analysis
Computer Programs
Uncle Sam thinks so, too.
Tax Deduction Information
Under IRS rules, taxpayers' building energy use performance must be
calculated using software that has been tested according to
ANSI/ASHRAE Standard 140 to qualify for tax deductions.
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“I have heard that LEED certified
buildings, which are designed based on
BPS, do often not save as much energy as
predicted by the simulation.”
Energy Performance of LEED Buildings
New Buildings Institute (NBI) Study (2008)
For all 121 LEED buildings the median measured EUI was 24%
below the CBECS national average [for 2003].
www.gbci.org/ShowFile.aspx?DocumentID=3598
Note: 552 LEED-NC buildings were certified in 2006
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Performance of LEED Buildings
LEED‐NBI data, CBECS matching by Newsham et al, 2009 Figure from Daylighting Handbook (Reinhart)
 LEED buildings have on average a 30% lower EUI (Energy Use Intensity).
 A third of LEED buildings had a higher EUI than their matched CBEC counterpart.
“If the energy use of LEED buildings can
be so high, did the computer simulations
predict this?”
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Comparison of measured and simulated
energy use for 97 LEED Buildings
Model uncertainty from weather, infiltration rate, usage schedule
and occupant behavior.
Paper
H W Samuelson, A Lantz and C F Reinhart, "Non-technical barriers to energy model sharing and reuse", Building
and Environment, 54, pp. 71-76, 2012.
But, models can be calibrated…
PhD thesis H W Samuelson (Harvard GSD, May 2013)
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Need for Commissioning
Source USGBC; Figure from Daylighting Handbook (Reinhart)
Today’s Presentation
From evaluating performance to
suggesting new forms
√ Environmental performance simulations are now capable of predicting
the physical performance of commonly used building typologies and
technologies.
 Parts of this analysis can be done by the designers themselves. Is this
true/desirable?
 How can we make sure that designers are using the tools accurately
and effectively?
 We are now moving toward whole city energy models in which
different energy flows are monitored are optimized.
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Teaching Modeling
Simulation
Checklist
Book Chapter: Reinhart C F,
“Simulation-based Daylight
Performance Predictions“ in
Building Performance
Simulation for Design and
Operation, Editors J Hensen
and R Lamberts, Taylor &
Francis, 2011
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Spring 2012 MIT 4.430 Daylighting
Spring 2012 MIT 4.430 Daylighting
 Simulation of 10.485.
 Practicing good simulation habits.
 Building trust in one’s own modeling skills.
Paper: Ibarra D, Reinhart C F, "TEACHING DAYLIGHT SIMULATIONS – IMPROVING MODELING
WORKFLOWS FOR SIMULATION NOVICES”, Proceedings Building Simulation 2013
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How do we reach the architects not
interested in taking electives on
simulation?
Question to Architects: “How frequently do simulation results influence the design of your buildings?”
Paper
H W Samuelson, A Lantz and C F Reinhart, "Non-technical barriers to energy model sharing and
reuse", Building and Environment, 54, pp. 71-76, 2012.
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The Game …
Human Cluster [Experts]
Simulation Queue
Searching for the winning combination…
C F Reinhart, T Dogan, D Ibarra and H W Samuelson, "Learning by doing - Teaching energy
simulation as a game", Journal of Building Performance Simulation, October 2011.
Simulation Order Form
Provided by Modeler
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Results: Final Designs
 EUIs of the 10 final designs were 22–31% below the base variant.
 Building massings chosen by the different groups diverged
significantly revealing that a performance based design analysis does
not necessarily lead to converging architectural solutions.
Today’s Presentation
From evaluating performance to
suggesting new forms
√ Environmental performance simulations are now capable of predicting
the physical performance of commonly used building typologies and
technologies.
√ Parts of this analysis can be done by the designers themselves. Is this
true/desirable?
 How can we make sure that designers are using the tools accurately
and effectively?
 We are now moving toward whole city energy models in which
different energy flows are monitored are optimized.
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Great, I am ready to try this, which
performance metrics shall I use to
improve my daylighting/passive
design?
Definition of a ‘well daylit space’
A space that is primarily lit with natural light and that combines a
high occupant satisfaction with the visual and thermal environment
with low overall energy use for lighting, heating and cooling.
Figure from Daylighting Handbook (Reinhart)
Paper: C F Reinhart and J Wienold, "The Daylighting Dashboard - A Simulation-Based Design Analysis
for Daylit Spaces", accepted for publication in Building and Environment, 2010.
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Daylighting Dashboard
Question: Should I specify blinds or not?
% Occupied hours
Daylit Area:
73% of the space
Potential glare: 50% of the year
44% of the space
no glare
View: 100% 50%
Paper
 Reinhart C F, J Wienold, “The Daylighting Dashboard - A Simulation-Based Design Analysis for Daylit Spaces”. Building and
Environment, 2011 46:2 386-396
DIVA for Rhino
Rhino Model
Climate‐based Metrics
Daysim
Annual Glare Maps
Daysim
Thermal Loads
EnergyPlus
Paper:
 K Lagios, J Niemasz and C F Reinhart, "Animated Building Performance Simulation (ABPS) - Linking Rhinoceros/Grasshopper with
Radiance/Daysim", Proceedings of SimBuild 2010, New York City, August 2010
 J A Jakubiec and C F Reinhart, DIVA-for-Rhino 2.0: Environmental parametric modeling in Rhinoceros/Grasshopper using
Radiance, Daysim and EnergyPlus, Proceedings of Building Simulation 2011, Sydney, November 2011
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So, now that we have an analysis framework for
daylighting. Let’s design something.
DIVA for Rhino
Grasshopper
Rhino Model
Visualizations
Radiance
Visual Comfort
Evalglalre
Climate‐based Metrics
Daysim
Radiation Maps
GenCumulativeSky
Annual Glare Maps
Daysim
Thermal Loads
EnergyPlus
Paper:
 K Lagios, J Niemasz and C F Reinhart, "Animated Building Performance Simulation (ABPS) - Linking Rhinoceros/Grasshopper with
Radiance/Daysim", SimBuild 2010, New York City, August 2010
 J A Jakubiec and C F Reinhart, DIVA-for-Rhino 2.0: Environmental parametric modeling in Rhinoceros/Grasshopper using
Radiance, Daysim and EnergyPlus, Building Simulation 2011, Sydney, November 2011
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Animated Building Performance Simulation
Simulation: J Niemasz and K Lagios
Zollverein by SAANA
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Environmental Performance
combined with a Genetic Algorithms
Determine a box shape with maximum enclosed volume and annual solar radiation
exposure in Boston (Niemasz)
This looks a bit like trying to have monkeys play
Shakespeare. Can we do better?
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SHADERADE - Generating the Next
Generation of Shading Systems
Conventional Shading
Non Performing Shading
SHADERADE
Design: Jeff
Niema
sz
How to Design a Static Shading System
Step 1 Identify the cooling period
Mar 21 – Sep 21
Solstices
Heating vs. Cooling Degree Days
Hourly Load Profile
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How to Design a Static Shading System
Step 2 Find a form that shades my window during cooling period
2 dimensional method
Autodesk Ecotect
Static Exterior Shading: SHADERADE
Surround Shade
Paper: J Sargent, J Niemasz, C F Reinhart, “SHADERADE: Combining Rhinoceros and EnergyPlus for the design of static exterior shading
devices”, Building Simulation 2011, Sydney, November 2011.
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Static Exterior Shading: SHADERADE
Once the volume has been assessed, any surface within its bounds can be visualized:
Surround Shade
Static Exterior Shading: SHADERADE
Trimming away regions with negative value (cutoff = 0):
Surround Shade
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Static Exterior Shading: SHADERADE
Horizontal and surround shades
Load optimized, 85% value trim:
Anchorage Boston Phoenix
Static Exterior Shading: SHADERADE
Horizontal and surround shades, Carbon optimized, 85% value trim:
(COP of 1.67, 0.83 for cooling, heating; carbon equivalent factors of 0.232, 0.758 kg/kWh for gas , electricity)
Anchorage Boston Phoenix
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Recent Developments
J Sargent & J Niemasz
Future Price Scenarios
S Holmes, C Reinhart
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Climate Change Predictions
A General Circulation Model (GCM) is a mathematical model of the general
circulation of a planetary atmosphere or ocean. [Wikipedia]
The IPCC Working Group III developed storylines which represent a potential
range of different demographic, social, economic, technological and
environmental developments (IPCC 2000).
Climate Change Weather File Generator
http://www.serg.soton.ac.uk/ccworldweathergen/index.html
Generates future climate files for locations worldwide (with limitations) with a
specific focus on the UK. It is based on the ‘morphing’ methodology.
Belcher SE, Hacker JN, Powell DS. Constructing design weather data for future climates. Building
Services Engineering Research and Technology 2005; 26 (1): 49-61.
Jentsch MF, Bahaj AS, James PAB. Climate change future proofing of buildings - Generation and
assessment of building simulation weather files. Energy and Buildings 2008; 40 (12): 2148-2168.
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How large is the Effect?
Harvard University – Gund Hall
Gund Hall now
DesignBuilder model
Samuelson, Holmes, Reinhart 2011
 33 Zone E+ model
 1990 TMY2 weather
data for Boston
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Case Study: Gund Hall now and then
36% less heating
 33 Zone E+ model
 1990 TMY2 weather
data for Boston
 predicted 2080 weather
data for the IPCCCA2
scenario (medium to high
emissions scenario).
45% more cooling
Linking Future Climate Files with Future Prices
Data Source: Economic Insights from Modeling Analyses of
H.R. 2454 — the American Clean Energy and Security Act
(Waxman-Markey); Pew Center for Global Climate Change
 The basic idea of the paper is to link 7 of the 22 energy price projections
from the 2009 Energy modeling Forum (EMF-22) to the four climate
change projections from the 3rd IPCC Assessment Report (TAR).
 The matching is realized via the Radiative Forcing (RF) of the different
scenarios. RF is the change in net irradiance at the top of the tropopause
compared to the year 1750.
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Case Study: Office Building in Boston
Generic 1980s office building, floor area 5000m2, 3
stories.
 Baseline: Building left as is.
 Minimum: Upgrade so that the building meets
ASHREA 90.1-2004 (more efficient HVAC and
windows (inoperable).
$89,000
upgrade ∆ cost
 Medium: Same as previous but add mixed-mode
ventilation & solar shading.
$183,000
upgrade ∆ cost
 Advanced: Same as previous but double all
insulation levels.
$255,000
upgrade ∆ cost
Case Study: Cumulative Energy Costs
Paper: S H Holmes and C F Reinhart, Assessing future climate change and energy price scenarios for
institutional building investment and HVAC operation, Building Research and Information, 41:2, pp.
209-222, 2013.
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Case Study: Cumulative Energy Costs
 IRR highest for minimum upgrade. (It is tough, energy is cheap in this
country.)
 Cooling dominated climates have higher IRRs. This does not necessarily
translate into actions today.
Today’s Presentation
From evaluating performance to
suggesting new forms
√ Environmental performance simulations are now capable of predicting
the physical performance of commonly used building typologies and
technologies.
√ Parts of this analysis can be done by the designers themselves. Is this
true/desirable?
√ How can we make sure that designers are using the tools accurately
and effectively?
 We are now moving toward whole city energy models in which
different energy flows are monitored are optimized.
31
Urban Modeling
C Reinhart, J A Jakubiec, T Dogan, T Rakha
Why should we care about energy use
in cities?
“In 2008, the world reached an invisible but momentous milestone:
For the first time in history, more than half its human population
[…] was living in urban areas. In 2050 there will be 9 billion
people.”
United Nations Population Fund
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Question: How to house 1.7 Billion City
Dwellers in 17 Years?
Solution 1: Let them figure it out
themselves.
Solution 2: Dense new
construction.
Informal Settlement in Rio
Suburbs in Shanghai
Cambridge Solar Map
 First combination of LiDAR data with advanced building
simulation modules
http://www.cambridgema.gov/solar/
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Photo of the MIT Campus (Google Maps).
LiDAR Data of the MIT Campus
3 dimensional point cloud (126 million points).
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GIS Model of Cambridge
City ArcGIS model: Building Footprints, Exterior Cladding Materials, Ground Composition,
Appraised Value and Renovations, Number of Floors, Building Type, Number of Bedrooms,
Year of Construction
3D Model of the MIT Campus
Generation of a 3D model through surface triangulation.
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Solar Radiation Map
Cumulative annual solar radiation.
Impact of Roof Temperature on Solar Cell
Efficiency
Paper: A Jakubiec and C F Reinhart, A Method for Predicting City-Wide Electricity Gains from Photovoltaic
Panels Based on LiDAR and GIS Data Combined with Hourly DAYSIM Simulations, Solar Energy (in press)
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Cambridge Solar Map
2008 - 2011
http://www.cambridgema.gov/solar/
How accurate are the results?
Annual Error 3.6%
Annual Error 5.3%
Paper: A Jakubiec and C F Reinhart, A Method for Predicting City-Wide Electricity Gains from Photovoltaic
Panels Based on LiDAR and GIS Data Combined with Hourly DAYSIM Simulations, Solar Energy (in press)
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(1) Get your Roof’s Solar Potential
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PV Potential for Cambridge
 If we were up to the challenge we could generate a
third of the city’s electricity use via PV.
 The cost would be in the order of $US 2.8 billion.
Urban Modeling
 Umi is a new urban modeling platform to design and improve new and
existing neighborhoods regarding multiple measures of urban
sustainability including operational energy use, daylighting, outdoor
comfort and sustainable transportation.
 Various effort are currently supported by:
 National Science Foundation
 US Department of Energy
 MIT Energy Initiative
 Transsolar Climate Engineering
 Government of Kuwait
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Umi Workflow
www.urbanmodeling.net
Rhino Model
1 Landmark building with explicit mixed land-use
2 Residential block and single family housing units
3 Irregular courtyard composition with massive block
4 Row houses (in a straight line)
5 Park
6 Massing composition
7 Narrow courtyard complex with a relative hi-rise
8 Explicit mixed land use with wider courtyard
9 Widest courtyard with low-rise arrangement
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Umi- Energy
Paper: C F Reinhart T Dogan, J A Jakubiec, T Rakha, A and A Sang, “UMI – An urban simulation
environment for building energy use, daylighting and walkability”, Building Simulation 2013,
Chambery, France, August 2013.
Umi- Daylight
Hourly Exterior Solar
Radiation (Daysim)
Interior light solver
Paper: T Dogan, C F Reinhart and P Michelatos, “Urban daylight simulation: Calculating the daylit
area of urban designs”, Proceedings of SimBuild 2012, Madison, Wisconsin, USA.
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Umi- Daylight
Computed in Less than 30 minutes
Paper: T Dogan, C F Reinhart and P Michelatos, “Urban daylight simulation: Calculating the daylit
area of urban designs”, Proceedings of SimBuild 2012, Madison, Wisconsin, USA.
Umi- Outdoor Comfort
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Umi- Mobility
Paper: T Rakha and C F Reinhart, “A carbon impact simulation-based framework for land use
planning and non-motorized travel behavior interactions”, Submitted to Building Simulation 2013,
Chambery, France, August 2013.
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Example in Umi
PassivHaus Neighborhood
Umi- Transportation
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Umi- Transportation
Umi- Finance
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Scorecard
Closing Thoughts
 Modern simulation engines can provide more actionable information
for energy saving measures to building owners, designers and
planning boards.
 More comprehensive teaching of these tools is required. One-day
workshop can only each individuals how to “press buttons”. It takes at
least a term to appreciate how the BPS may truly inform design.
 We need new and more good performance metrics.
 Soon we will see whole city energy models in which different energy
flows are monitored are optimized.
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Thank You
Contact
Christoph Reinhart
Associate Professor
Massachusetts Institute of Technology
Email: creinhart@mit.edu
MIT Sustainable Design Lab
Carlos Cerezo
Karthik Dondeti
Timur Dogan
Diego Ibarra (GSD)
Alstan Jakubiec
Tarek Rakha
John Sullivan
Solemma LLC
Alstan Jakubiec
Kera Lagios
Jeff Niemasz
Jonnie Sargent
mapdwell LLC
Eduardo Berlin
Alstan Jakubiec
Alumni
Seth Holmes, Elliot Glassman, Cynthia Kwan, Rohit Manudhane, Rashida Mogri, Azadeh
Omidfar, Debashree Pal, Tiffany Otis, Holly W Samuelson, Devon Sparks, Jennifer Sze
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