RESEARCH STRATEGY FOR MANUFACTURED HOMES
HEAT PUMP RELATED MEASURES
AUGUST, 2015
1. INTRODUCTION
This Research Strategy aims to better understand the per unit energy savings for the following
RTF Planning UES Manufactured Home (MH) measures:
● Heat Pump Conversions: Converting primary heat source from Electric Resistance
Forced Air Furnace (FAF) to an Air Source Heat Pump (HP). Baseline for this measure is
Pre-Conditions.1
● Weatherization Measures with HP as measure identifier: Attic, floor, window, and
infiltration reduction measures with Pre- Conditions baseline.
● Performance based duct sealing: Baseline is Pre-Conditions, efficient case “meets RTF
standards.2”
● Commissioning Controls and Sizing (CC&S): CC&S of HP in MH has a dedicated Research
Strategy3 approved by the RTF. Data identified by the CC&S Research Strategy can be
collected through the data collection methods outlined in this document. Combining
these data collection efforts will provide significant cost and effort efficiency. The
baseline for this measure is Current Practice.
● Heat Pump Upgrades: Upgrading existing Air Source HP to a higher efficiency Air Source
HP. The baseline for this measure is Current Practice (8.5 HSPF, 14 SEER), and the
efficient case is a high efficiency (9.0 HSPF, 14 SEER) HP. This is a Current Practice
measure.
These measures are collectively referred to as “MH HP Related Measures” in this Research
Strategy4. A single Research Strategy is developed for all MH HP Related Measures as (1) the
data collection and analysis steps required to develop UES for all MH HP Related Measures are
similar, (2) more than one MH HP Related measure is usually installed in a home, and (3) these
measures significantly interact with each other making it difficult to calculate savings in
1 Baseline for each measure in this Research Strategy is defined as per the RTF Guidelines: “Roadmap for the Assessment of
Energy Efficiency Measures”, Section 3.2
2 http://rtf.nwcouncil.org/measures/res/ResPerformanceDuctSealingSpec_v3_1.docx
3 http://rtf.nwcouncil.org/measures/res/ResearchStrategyCCS.docx
4
The total savings potential from these measures is roughly estimated to be at least 20 aMW
Research Strategy: MH HP Related Measures
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isolation. Ecotope Inc. have conducted research in the region to better understand HP behavior
in MH in 1995 (Manufactured Homes Acquisition Program: Analysis of Program Impacts) and
2005 (Heat Pump Installation Practices and Performance). Funders and prospective researchers
should understand the challenges faced by these studies, and their findings before drafting a
detailed research plan5.
2. RESEARCH OBJECTIVE
The objective of this research strategy is to estimate the energy savings for each of the MH HP
Related Measures. This research strategy recommends developing UES estimates through
billing analysis and calibrated building energy simulation models.
Determining energy savings for MH HP Related Measures is complex due to significant
interaction between these measures. RTF staff developed an engineering model calibrated to
the observational Residential Building Stock Assessment (RBSA)6 and presented it to the RTF at
the June 2015 meeting7. The RTF determined that the RBSA based calibrated engineering model
did not provide reliable annual energy savings estimates for MH HP Related Measures. An
engineering model calibrated to an experimental dataset (actual program data) may help
develop a more accurate estimate of annual energy savings.
The highest priority measure for this Research Strategy is the HP Conversion Measure. Although
this measures is expected to have high savings potential, a signal of energy savings was not
apparent in the analysis conducted by the RTF Contract Analyst Team (CAT) on Idaho Power
Low Income MH program data and presented to the RTF in June, 2015 5.
3. DATA COLLECTION AND ANALYSIS
This section provides a strawman data collection and analysis approach as a means of sketching
out one potential path to provide better clarity around the research objective defined above.
The RTF recognizes that there may be alternative approaches to meeting this objective. The
data collection activities consist of billing data collection and field audits for all homes in the
sample.
Field audits should collect all parameters required to construct representative pre- and postcase building energy simulation models. This includes building shell characteristics (house
square footages, floor, wall, and ceiling insulation levels, window type, whole-house air
leakage), duct leakage, and heat pump system characteristics (e.g., resistance heat control
settings, air flow across indoor coil, sizing). The data collected from this audit will be used to
develop energy simulation modeling inputs to represent baseline and efficient-case measures.
5
Some of the challenges faced by these studies could be solved using interval/sub-meter data. This would be a more cost intensive
method to evaluate the MH HP Related Measures
6 The RBSA is an observational dataset that was used to construct a curve that calibrates the heating energy consumption for a
home in a given climate zone. The RBSA is available at: http://neea.org/resource-center/regional-data-resources/residentialbuilding-stock-assessment
7 RTF June, 2015 Meeting: http://rtf.nwcouncil.org/meetings/2015/06/
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Table 1 presents a summary of the data collection activities recommended by this Research
Strategy.
Table 1 Recommended Sample Size8 to Determine UES for HP Related Measures
Measurea
HP Conversion
Sample Sizeb
c
Billing Data
120
Required
Pre and Post
50
Required
Pre and Post
120 baseline +
120 participants
Required
Post Only
(Participants)
50
Required
Pre and Post
Performance Based Duct
Sealing
CC&Sd
Field Audits
Weatherization
HP Upgrade
Optionale
Optional; Post
Only
a: Homes selected in the sample may have more than one MH HP Related Measure
b: Minimum recommended sample size
c: The sample should strike a balance between well and poorly weatherized homes.
d: For the CC&S measure, the sample size includes both single family and MH
e: Data collected for the HP Conversion measure can be applied to calculate HP Upgrade energy savings
Once billing and field audit data are collected, the following data analysis activities will be
conducted:
Conduct billing analysis: Determine pre- and post-annual space heating energy use through
billing analysis; this estimated space heating energy use is a calibration check on the outputs of
the energy simulation model and used to adjust the energy simulation model input parameters9
(developed in the next steps of this Research Strategy). The billing analysis will provide
estimates of savings from the measure or bundle of measures installed at each home.
Construct calibrated engineering models: Construct calibrated engineering simulation models
for the pre-10 and post-case homes in the sample using field audit data and appropriate utility
bill data. Calibrated engineering simulation models enable the development of average UES for
the region for individual MH HP Related Measures. The details of the calibration methodology
should be determined once engineering models based on field audit data are constructed and
billing analysis performed.11
The CC&S measure savings are calculated using baseline (non-participant) and participant
practices regarding CC&S; the CC&S Research Strategy explains data analysis for this measure in
8 The Washington Department of Labor and Industrial Services MH database is a potentially useful resource to identify the study
sample.
9
Although implied in this Research Strategy, the authors want to clarify that collecting billing data and conducting a billing analysis
(described in the data collection activities later in this section) to confirm existence of energy savings should be done before
completely implementing this Research Strategy.
10 Note: An RTF approved calibration for electric FAF homes already exists, and it is recommended to be tested (at a minimum) on
the base case for the HP Conversion measure.
11 Two (of many possible) examples of engineering model calibration are (1) developing on-average calibration factors for prehomes and post-homes respectively, and (2) developing an average thermostat setting based on location.
Research Strategy: MH HP Related Measures
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more detail3. The HP Upgrade measure assumes that an a more efficient HP will consume less
energy, this energy savings can be modeled. Both of these measures, HP Upgrades and CC&S,
require post-only billing data. The researcher should construct base case models using the field
audit data and the calibrated post-case energy simulation model.
Calculate energy savings: Once estimated, the calibrated base and efficient case engineering
models can be used to generalize energy savings and develop RTF UES for each MH HP Related
Measure. The methodology to develop RTF UES Measure savings for each MH HP Related
Measures would be determined by the author of the study based on the results of the data
analysis and the calibration12. If, upon conducting the analysis, the researcher determines that
construction of calibrated energy simulation models is not feasible, then the energy savings
calculated through the billing analysis could be used to develop RTF UES savings.
4. ESTIMATED COST RANGE
(< $25k) ($25k-$100k) ($100k-$250k) ($250k-$500k) ($500k-$1MM) ($1MM-$2MM) (> $2MM)
The RTF estimates the research described above would range between $500k and $1 million.
This cost is mainly driven by the field audits recommended in Section 3. Estimated cost would
be closer to $1 million if CC&S were to be completely studied as a part of this research, and
closer to $500k if CC&S were considered as separate research.
12 Example 1: Apply the RTF Savings Guidelines compliant measure interaction savings calculation methodology (RTF Guidelines
for Energy Savings Section 2.3.3.4) to calculate energy savings for each installed MH HP Related Measure per home. Individual
home MH HP Related Measure savings shall be appropriately weighted across the sample to calculate weighted average UES.
Example 2: Use the calibration results of this study to develop RTF prototype SEEM models to calculate UES for each MH HP
Related Measure.
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