quantitative analysis of electricity savings from the home ... · savings in gas or other fuel...

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INSTITUTE FOR SUSTAINABLE FUTURES

QUANTATIVE ANALYSIS OF ELECTRICITY SAVINGS FROM THE HOME SAVER REBATES PROGRAM

2013

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Prepared by

THE INSTITUTE FOR SUSTAINABLE

FUTURES, UTS

For

NSW Office of Environment

and Heritage

September 2013

QUANTATIVE ANALYSIS OF

ELECTRICITY SAVINGS

FROM THE HOME SAVER

REBATES PROGRAM

FINAL REPORT JULIAN FYFE, STEVE MOHR, GEOFF MILNE, PETER RICKWOOD

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ABOUT THE AUTHORS

The Institute for Sustainable Futures (ISF) was established by the University of Technology, Sydney in

1996 to work with industry, government and the community to develop sustainable futures through

research and consultancy. Our mission is to create change toward sustainable futures that protect

and enhance the environment, human well-being and social equity. We seek to adopt an inter-

disciplinary approach to our work and engage our partner organisations in a collaborative process

that emphasises strategic decision-making.

For further information visit: www.isf.uts.edu.au

Research team: Julian Fyfe, Steve Mohr, Geoff Milne and Peter Rickwood

CITATION

Please cite this report as:

Fyfe, J., Mohr, S., Milne, G., Rickwood, P. 2013, Quantitative analysis of electricity savings from the

Home Saver Rebates Program, prepared for the NSW Office of Environment and Heritage by the

Institute for Sustainable Futures, UTS.

DISCLAIMER

While all due care and attention has been taken to establish the accuracy of the material published,

UTS/ISF and the authors disclaim liability for any loss that may arise from any person acting in

reliance upon the contents of this document.

REVIEW

Version Author Reviewed by Date

Interim Draft GM and JF JF 8 July 2013

Draft GM and JF JF 24 July 2013

Draft GM and JF OEH 12 August 2013

Final GM and JF JF 2 September 2013

http://www.isf.uts.edu.au/

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EXECUTIVE SUMMARY

The NSW Office of Environment and Heritage (OEH) commissioned the Institute for Sustainable

Futures (ISF) to undertake a quantitative statistical analysis of electricity savings from the installation

of hot water systems as a result of rebates provided under the Home Saver Rebates Program (HSRP).

Savings in gas or other fuel usage were not examined.

The program targeted all households in NSW. More than 330,000 rebates were granted statewide,

which represents about 1 in 8 NSW households. This analysis only evaluates participating houses in

the Ausgrid electricity distribution area.

Ausgrid provided electricity consumption data for 51,358 households in their service area that

received hot water rebates, of which 38,455 were able to be used in the analysis. Sample sizes were

reduced to the numbers given in Table 1 through data filtering. Ausgrid also supplied consumption

data for approximately 1.38 million households that did not participate in HSRP to provide a control

group for the analysis.

The primary methodology used to analyse the savings was a ‘mixed effects’ conditional demand

analysis (CDA) model, as this is the most widely used approach to analysing energy efficiency

programs (Vines & Sathaye 1999, Bartels and Fiebig 2000, Isaacs et al 2006) and is capable of

controlling for influences such as climate and household specific factors that can affect apparent

savings. The matched pairs mean comparison (MPMC) methodology was also used where

appropriate to validate the regression findings. The results of the regression are reported in the

body of the report and the detailed MPMC results can be found in Appendix B. Technical

descriptions of the two methodologies, including the regression equations used, can be found in

Appendices C and E.

A summary of the average net annual savings in total electricity consumption across all tariffs and

system types obtained from the mixed modelling analysis can be seen in Table 1.

The savings achieved are affected by a number of factors such as household occupancy and the

passage of time. As would be expected, household occupancy had a significant impact on the

electricity savings resulting from replacing hot water systems. Energy used for hot water is positively

correlated with the number of people in a household, which also means that larger households

benefit from greater energy savings with the switch away from electric storage systems. The

exception to this was for electric-boosted solar systems where the savings declined slightly with a

household occupancy of more than two people. The impact of occupancy is investigated in Section

5.6 of this report.

Overall, the savings also declined slightly over time by about 0.5kWh/day for each year post the

installation of the new hot water system, but this is dependent on the system type. This is in part

due to the decline in residential electricity consumption over recent years. Again this is investigated

in more detail in Section 5.6.

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Table 1 Average net program electricity savings by type of rebated hot water system

Rebate Item Average Household Saving % of household

consumption

Sample size -

number of households

kWh/d kWh/year

Hot water systems

Gas – instantaneous or storage

7.40 ± 0.03 2,717 ± 12 29 ± 0.1 8,698

Gas-boosted Solar 6.43 ± 0.09 2,348 ± 33 25 ± 0.3 1,252

Electric-boosted Solar 4.11 ± 0.02 1,500 ± 9 16 ± 0.1 15,682

Heat Pump 3.69 ± 0.03 1,347 ± 10 14 ± 0.1 12,715

A comparison of the tariff data from the OEH rebate forms and metered data from Ausgrid showed

that many people have a poor understanding of which electricity tariff they are actually using,

particularly when tariff changes were involved. For example, approximately half of those who

remained on a continuous tariff after the installation of a new hot water system erroneously

reported that they had changed to an off-peak tariff. Such a low level of awareness is likely to be a

barrier to encouraging people to take up options to reduce their electricity costs.

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Contents

EXECUTIVE SUMMARY ............................................................................................................................ 4

1 THE HOME SAVER REBATES PROGRAM ........................................................................................ 10

1.1 Program Description ............................................................................................................. 10

1.2 Characteristics of HSRP Hot Water Systems ......................................................................... 13

2 ENERGY SUPPLY AND TARIFF SWITCHES ....................................................................................... 14

2.1 Supply Analysis ...................................................................................................................... 14

2.2 Tariff Analysis ........................................................................................................................ 17

3 BACKGROUND INFLUENCES ON ELECTRICITY CONSUMPTION ..................................................... 20

3.1 Electricity Consumption Trends ............................................................................................ 20

3.2 The Take-Back Effect ............................................................................................................. 22

4 METHODOLOGY ............................................................................................................................ 23

4.1 Data Pre-Processing .............................................................................................................. 23

4.1.1 Filtering ......................................................................................................................... 24

4.1.2 Intervention date .......................................................................................................... 25

4.2 Mixed Effects Modeling in R ................................................................................................. 25

4.2.1 Overall Savings and Controlled Load Subsets ............................................................... 26

5 SAVINGS ESTIMATES ..................................................................................................................... 28

5.1 Households that installed a gas hot water system ............................................................... 30

5.2 Households that installed a gas-boosted solar HWS ............................................................ 32

5.3 Households that installed electric-boosted solar HWS......................................................... 33

5.4 Households that installed a heat pump hws ........................................................................ 35

5.5 Comparison with Energy Australia findings for hws replacement ........................................ 35

5.6 The effect of occupancy and time on HWS savings .............................................................. 36

6 VALIDATION OF MIXED MODELLING RESULTS USING MPMC ...................................................... 38

7 COMMENTARY ON HSRP DATA ..................................................................................................... 39

8 CHANGES TO PARTICIPANTS’ ELECTRICITY BILLS .......................................................................... 40

9 CONCLUSIONS AND RECOMMENDATIONS ................................................................................... 42

10 REFERENCES .............................................................................................................................. 44

APPENDIX A: Breakdown of hot water systems installed by manufacturer and type .......................... 45

APPENDIX B: Detailed tariff analysis ..................................................................................................... 49

APPENDIX C: Mixed effects model specifications ................................................................................. 52

APPENDIX D: Mixed modelling outputs (from R) .................................................................................. 55

Overall savings model (model specification 2) ................................................................................. 55

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MODEL INCORPORATING OCCUPANCY AND SAVINGS DECAY (model specification 4) ................... 56

OFF PEAK SYSTEM REPLACEMENT SUBSET – NET SAVINGS (model specification 2) ........................ 60

OFF PEAK SYSTEM REPLACEMENT SUBSET – controlled load SAVINGS (model specification 2) ..... 62

APPENDIX E: MPMC methodology and detailed results ....................................................................... 64

Households that switched to gas hot water ................................................................................. 64

Households that switched to gas-boosted SOLAR hot water ....................................................... 65

Households that switched to electric-boosted solar .................................................................... 66

Households that installed a heat pump ........................................................................................ 67

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List of figures

Figure 1-1 Uptake of the HSRP hot water system rebates .................................................................. 11

Figure 1-2 Map of the Ausgrid distribution area .................................................................................. 12

Figure 1-3 Installation of hot water systems by type ........................................................................... 13

Figure 2-1 Before and after intervention electricity consumption of participants who report staying

on an off-peak tariff .............................................................................................................................. 15

Figure 2-2 Before and after intervention electricity consumption of participants who report switching

from continuous to controlled load ...................................................................................................... 16

Figure 2-3 Histogram of before and after controlled load consumption for participants claiming to

switch from controlled load to continuous tariff but still have controlled load consumption ............ 17

Figure 2-4 Histogram of before and after consumption for participants switching from controlled

load to gas but still have controlled load consumption ........................................................................ 17

Figure 3-1 Trend in average monthly electricity consumption of non-participant households over

timeframe of HSRP ................................................................................................................................ 20

Figure 3-2 Energy Australia regulated Domestic All Time residential electricity tariff ........................ 21

Figure 5-1 Average overall savings and savings from replacement of off peak HWSs by system type 29

Figure 5-2 Average daily saving over time for gas HWS ....................................................................... 31

Figure 5-3 Average daily saving over time for electric-boosted solar HWS.......................................... 34

Figure 5-4 Variation in average monthly solar exposure for Sydney 2009-2012. ................................ 34

Figure 5-5 Saving by HWS and household occupancy 3 months after installation .............................. 37

Figure 5-6 Saving by HWS and household occupancy 24 months after installation ............................ 37

Figure C-1 Mixed model specification flow chart. Numbers correspond to models listed in Table A-1.

Red lines indicate REML log likelihood ratio tests, blue maximum likelihood log likelihood ratio tests,

and green model outputs. .................................................................................................................... 54

Figure E-1 Pre and post intervention consumption of gas HWS participant and matched pair control

cohorts .................................................................................................................................................. 65

Figure E-2 Gas HWS savings time series ............................................................................................... 65

Figure E-3 Pre and post intervention consumption of gas-boosted solar HWS participant and

matched pair control cohorts ............................................................................................................... 66

Figure E-4 Gas-boosted solar HWS savings time series ........................................................................ 66

Figure E-5 Pre and post intervention consumption of electric-boosted solar HWS participant and

matched pair control cohorts ............................................................................................................... 67

Figure E-6 Electric-boosted solar HWS savings time series .................................................................. 67

Figure E-7 Pre and post intervention consumption of heat pump HWS participant and matched pair

control cohorts ...................................................................................................................................... 68

Figure E-8 Heat pump HWS savings time series ................................................................................... 68

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List of tables

Table 1 Average net program electricity savings by type of rebated hot water system ....................... 5

Table 1-1 Timeframes and amounts offered by HSRP rebates for water heaters ............................... 10

Table 1-2 Numbers of participant households eliminated from and used in the analysis ................... 13

Table 2-1 Summary of agreement between OEH and Ausgrid supply information ............................. 14

Table 2-2 Percentage of households who nominated the correct original tariff ................................. 18

Table 2-3 Summary of discrepancies in the tariff data ......................................................................... 19

Table 3-1 Average daily participant household consumption pre-intervention by tariff ..................... 21

Table 3-2 Average pre-intervention electricity consumption by rebate item ...................................... 22

Table 4-1 Ausgrid electricity consumption tariffs and corresponding supply categories. ................... 24

Table 5-1 Average net electricity saving due to replacement HWS..................................................... 28

Table 5-2 Average net electricity saving due to replacement of off peak HWSs (on controlled load

supply). .................................................................................................................................................. 29

Table 5-3 Average electricity saving for households that installed a gas HWS ................................... 30

Table 5-4 Average electricity saving for households that installed a gas-boosted solar HWS ............ 32

Table 5-5 Average saving in total electricity per household that installed a electric-boosted solar

HWS ....................................................................................................................................................... 33

Table 5-6 Average electricity saving for households that installed heat pump HWS ........................... 35

Table 6-1 Comparison of regression and MPMC saving results ........................................................... 38

Table 8-1 Energy Australia regulated electricity prices 2012-2013, c/kWh ......................................... 40

Table 8-2 Average annual electricity bill saving for different hot water systems ................................ 41

Table C-1 Model equations tested in the HWS model step-up specification process .......................... 52

Table C-2 Variables used in the HWS mixed modeling for total electricity consumption .................... 53

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1 THE HOME SAVER REBATES PROGRAM

1.1 PROGRAM DESCRIPTION

The NSW Home Saver Rebates Program (HSRP) provided rebates to NSW households to purchase

items to reduce their energy and water use, including:

Rainwater tanks

Solar, heat pump and gas hot water systems replacing electric systems

Replacement of single-flush toilets with new 4 star dual flush toilet systems

Hot water recirculation units for instantaneous gas hot water heaters

Ceiling insulation

Water efficient washing machines

The program operated between 1 July 2007 and 30 June 2011. During that time, a total of 332,239

households obtained a rebate, equivalent to one in eight (12.5%) households in NSW.

This study quantifies the electricity savings for households that received a NSW Home Saver Rebate

for a replacement hot water system (HWS). The rebates that were made available for HWSs are

given in Table 1-1. The total number of rebates paid for HWSs was 155,216. Figure 1-1 shows the

number of rebates paid for hot water systems over the life of the HSRP (Source: OEH rebate

database).

Table 1-1 Timeframes and amounts offered by HSRP rebates for water heaters

Timeframe Rebate item Rebate amount

1/10/07-15/01/10 Gas 5 Star instantaneous / storage HWS

$300

Solar / Heat pump HWS 20-27 RECs

$600

Solar / Heat Pump HWS 28-35 RECs

$800

Solar / Heat Pump HWS 36-43 RECs

$1,000

Solar / Heat Pump HWS 44+ RECs

$1,200

15/01/10-30/06/11 Gas 5 Star instantaneous / storage OR solar HWS

$300

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Figure 1-1 Uptake of the HSRP hot water system rebates

Households that had net solar electricity meters and those that had participated in the OEH Home

Power Saver Program or had obtained an HSRP rebate for ceiling insulation at any stage within the

analysis window were excluded from the analysis to ensure savings estimates for the rebate

programs were not biased by households known to be engaging in other electricity saving activities.

The savings analysis covers households within the distribution area of Ausgrid only (shown in Figure

1-2). It does not consider the rest of NSW. Since the performance of HWSs is influenced by climate

to some extent, this means that the results are specific to the climate zones contained within this

geographical area.

Ausgrid provided consumption data for 51,358 households that received a hot water system rebate,

of which 38,347 were able to be used in the analysis. As shown in Table 1-2, the bulk of households

were eliminated from the analysis because no National Metering Identifier (NMI)1 was provided

(10,695), no Delivery Point Identifier (DPID)2 was provided (1,387) or they are a net generation solar

PV customer (3,338).

1 Unique identification number for all electricity connection points in Australia

2 Unique identification number for all mailing addresses in Australia

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Figure 1-2 Map of the Ausgrid distribution area

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Table 1-2 Numbers of participant households eliminated from and used in the analysis

HSRP Participants

Number of hot water rebates3 51,358

No DPID provided 1131

No NMI in Ausgrid lookup 8800

Net Generation solar customer 2926

No Intervention date 1

Ausgrid and OEH address mismatch 18

Signed up to the same program multiple times

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Only has a controlled load meter 1

Total removed from analysis 12,903

Valid Participants 38,455

1.2 CHARACTERISTICS OF HSRP HOT WATER SYSTEMS

Figure 1-3 shows the numbers of participant households that installed different types of hot water systems based on the raw OEH rebate form data. Although only approximately 1200 solar gas boosted systems were installed, the magnitude of the savings for hot water systems means that this number is adequate to provide a reliable estimate of the associated electricity saving. A detailed breakdown of the various manufacturers and types of hot water systems installed under the HSRP is given in Appendix A.

Figure 1-3 Installation of hot water systems by type

3 Excluding households that also received a rebate for ceiling insulation.

8912

1283

15905

12923

Gas Solar Gas Solar Electric Heat Pump

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2 ENERGY SUPPLY AND TARIFF SWITCHES

Recipients of HWS rebates had to identify their original HWS electricity tariff, the new type of HWS

system installed, and in the case of new electric HWS systems (heat pumps and electric-boosted

solar) the tariff applied to the new system. As a check on the veracity of the data collected by OEH

from the rebate forms, switches between tariffs or energy sources (electricity to gas) that could be

inferred from the data supplied by Ausgrid (by analysing the presence or otherwise of off-peak tariffs

before and after HWS system installation) were compared with those recorded by OEH.

2.1 SUPPLY ANALYSIS

Table 2-1 provides a summary of the switches between different types of HWS energy supply and

the agreement between OEH and Ausgrid data for participants who replaced a HWS. Electricity

tariffs have been grouped into ‘continuous’ supply (C), incorporating the standard continuous tariff

and all time of use tariffs, and ‘controlled load’ supply (CL), incorporating both off-peak tariffs.

Continuous supply incorporates the uninterrupted supply to the bulk of, if not all (where a controlled

load meter is not in place) household demand. Controlled load supply is separately metered, and

supplies time-controlled power only during lower demand periods (such as overnight or early

morning) typically to HWSs, but in some cases may also supply other appliances that do not require

continuous supply such as pool filter pumps. The category ‘electric’ supply (E) is applied to

households that did not report either their pre- or post-intervention tariff on the rebate form, or for

which there was no Ausgrid consumption data before or after the installation. The ‘gas’ category in

Table 2-1 includes both conventional gas and gas-boosted solar HWSs. In the Ausgrid data, a new gas

supply was inferred from a lack of an off-peak load tariff post intervention.

There were significant discrepancies between both pre- and post- intervention HWS energy supplies

reported by the participant households in the OEH data and the metered consumption data

provided by Ausgrid, indicating that many households have a poor understanding of which electricity

tariffs they are using.

Table 2-1 Summary of agreement between OEH and Ausgrid supply information

Change in HWS energy supply

Number in OEH data

Number in Ausgrid data

Percentage overlap

#

CL to CL 21,794 21,347 85.9

CL to C 856 2,547 23.5

C to CL 1,004 507 6.8

C to C 4,414 4,294 59.8

CL to E 12 0

C to E 45 0

E to CL 6 0

E to C 66 0

E to E 761 5 0

CL to G 5,911 5,349 68.5

C to G 3,456 3,080 66.1

E to G 829 1,767 18.5

Total 39,025 39,025 72.0 # Percentage of OEH participants that are in agreement with the Ausgrid data.

Note: C = continuous supply, CL = controlled load supply , E = tariff indeterminate, G = gas

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The best agreement is for participants where the HWS remained on a controlled load supply, with

nearly 86% agreement. The worst agreement was with people changing from continuous to

controlled load with less that 7% agreement. The agreement in households remaining on a

controlled load supply is illustrated in Figure 2-1, which shows average electricity consumption

before and after system replacement using Ausgrid data (left) and OEH data (right) to define hot

water tariffs . The blue and red lines represent average household consumption while the vertical

line at ‘0’ on the horizontal axis demarks the month of system replacement against which individual

household consumption records were aligned. The two plots exhibit very similar before and after

consumption patterns, both showing substantial decreases in the controlled load electricity

consumption following replacement, with no apparent change in continuous supply consumption,

indicating that these participants reported their tariff change correctly.

Ausgrid data OEH data

Figure 2-1 Before and after intervention electricity consumption of participants who report staying on an

off-peak tariff

By comparison Figure 2-2 shows the electricity consumption of participants who shifted from

continuous to controlled load supplies. Amongst the switches identified using the Ausgrid data, the

consumption pattern is as expected; controlled load consumption in the pre period is close to zero

and is followed by a rise in the after period, while the continuous consumption decreases at the

installation date. Furthermore, the percentage of households on controlled load supply increases to

100% after the intervention month. In the case of tariff switches reported in the OEH data, more

than 50% of households who say they shifted from continuous to one of the off-peak tariffs actually

register controlled load consumption prior to the installation of the replacement HWS, which

averages close to what a HWS system typically uses. In addition, both the controlled load and

continuous consumption decrease after the installation, which shows that the cohort includes a mix

of HWS energy supply switches rather than just the nominated switch from continuous to controlled

load supply.

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Ausgrid data

OEH data

Figure 2-2 Before and after intervention electricity consumption of participants who report switching from

continuous to controlled load

The supply changes determined from the Ausgrid data appears to be consistent with actual change

to the tariffs in the households, while the OEH data is highly variable with some tariff changes

indicating a good agreement with Ausgrid data (e.g. controlled load to controlled load), and other

tariff changes (e.g. continuous to controlled load) having a very poor level of agreement.

A significant number of households who indicated on their rebate form that they had switched to

either gas or a continuous tariff also showed some controlled load consumption post the installation,

indicating that in some cases they did not actually switch to gas or continuous electricity supply, or

that the household had a secondary HWS or other appliances, such as a pool filter pumps, connected

to the controlled load supply. Figure 2-3 shows a histogram of the consumption data for the

participants who said they went from continuous to controlled load supply. It can be seen that these

households were in fact still consuming some off-peak electricity after the switch to continuous hot

water. The same is the case for participants that stated they switched to a gas or gas-boosted solar

HWS (Figure 2-4).

Assuming that most households accurately filled out the field describing the type of HWS installed

(since it has significant implications for the cost and rebate available to the customer), the average

controlled load consumption recorded after installation by households that stated they switched to

gas systems (1.1 kWh/d) could be considered to reflect consumption from a secondary HWS or other

appliances such as pool filter pumps. The noise in this data is substantial (standard deviation of 15.1

kWh/d) and would in part arise from the various types and modes of operation of HWSs and other

appliances connected to the controlled load supply, but also due the (presumably small) fraction of

households that incorrectly identified the type of HWS installed.

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Figure 2-3 Histogram of before and after controlled load consumption for participants claiming to switch

from controlled load to continuous tariff but still have controlled load consumption

Figure 2-4 Histogram of before and after consumption for participants switching from controlled load to gas

but still have controlled load consumption

2.2 TARIFF ANALYSIS

Analysis of specific tariff data (as opposed to supply category) shows that many households have a

poor understanding of which electricity tariff(s) they are using. As detailed in Table 2-2, some 28% of

participants incorrectly nominated their pre-intervention tariff (a statistic unaffected by the new hot

water system type field). The biggest confusion with existing tariffs lay with households who were

actually on an off-peak 2 tariff but reported being on off-peak 1 (36%). The second largest error was

for the 20% of households who were on a continuous tariff but reported being on off-peak 1. The

highest level of agreement was with households who were on an off-peak 1 tariff, where 79% of

households reported it correctly. Full breakdowns of tariff switches by HWS type according to the

OEH and Ausgrid data sets are given in Appendix B.

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Table 2-2 Percentage of households who nominated the correct original tariff

Actual original tariff (Ausgrid)

Reported original tariff (OEH)

Continuous Off-peak 1 Off-peak 2 Electric

(not reported)

Continuous 70 20 7 3

Off-peak 1 5 79 12 4

Off-peak 2 5 36 54 4

Electric 3 31 32 34

TOTAL 22 55 18 4

The data presented in Table 2-3 shows a further analysis of the tariff changes and the main reasons

for the discrepancies found. The three left hand columns shows what was recorded on the OEH

rebate form as the before and after tariff as a result of a hot water system replacement. Columns 4

and 5 show the agreement with the Ausgrid data. The following columns show the three most

common forms of discrepancy between the Ausgrid and OEH data.

It can be seen that many households do not realise that they are changing electricity tariffs. For

example, 45% and 38% of households thought they were staying with OP1 or OP2 respectively,

despite switching between the two controlled load meters. Similarly 51% and 43% of households

thought they were remaining on the continuous tariff, despite switching to OP1 or OP2 respectively.

Households that received a gas or solar gas system appear to be more aware of their electricity

metering arrangements. For example, 76%, 53% and 77% of households correctly identified being on

OP1, OP2 or Continuous tariff before installing a gas system. By comparison, 68%, 48% and 68% of

households correctly identified going to continuous electricity when on OP1, OP2 or Continuous in

the before period. Hence households that installed a gas system are consistently more correct than

other households. Part of this difference could be explained by households who stayed on electricity

for hot water not realising that they are changing tariffs, whereas with gas the change is much more

obvious.

It should be noted Table 2-3 was constructed based on the assumption that the field describing the

type of HWS installed was 100% accurate. While this is unlikely to be the case, it can be reasonably

assumed that the degree of accuracy is much higher than the tariff fields given that the selection of

the type of system has a significant bearing on cost and would in most cases require some

engagement from the participant. Accordingly, Table 2-3 may be considered a good indication of the

level of understanding in relation to tariff changes amongst the participant population.

With only 52% overall agreement between OEH and Ausgrid data, it is clear that many householders

are not familiar with or do not understand their electricity tariffs.

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Table 2-3 Summary of discrepancies in the tariff data

OEH tariff change

OEH form Ausgrid data agrees Largest discrepancy 2nd largest discrepancy 3rd largest discrepancy

Number of households

% of all participants

Number of households

% of

households

Ausgrid Tariff

change

% of

households

Ausgrid Tariff

change

% of

households

Ausgrid Tariff

change

% of

households

OP1 to OP1 15117 39 7088 46 OP1 to OP2 18 OP1 to C 11 OP2 to OP2 7

OP1 to OP2 977 3 236 24 OP1 to OP1 45 OP2 to OP2 11 C to C 7

OP1 to CTS 655 2 449 68 C to C 13 OP2 to C 10 OP1 to OP2 6

OP1 to G 4860 12 3715 76 OP2 to G 13 C to G 8 E to G 1



OP2 to C 201 1 98 48 OP1 to CTS 29 C to CTS 11 OP2 to OP1 3

OP2 to G 1051 3 563 53 OP1 to G 36 C to G 7 E to G 3

C to C 4414 11 3019 68 C to OP1 8 OP1 to C 7 C to OP2 5

C to OP1 786 2 251 31 C to C 51 C to OP2 11 C to E 1

C to OP2 218 1 52 23 C to C 43 C to OP1 19 OP1 to OP2 5

C to G 3456 9 2675 77 OP1 to G 15 OP2 to G 7 E to G 0

E to E 761 2 26 3 OP1 to OP1 28 C to C 14 OP1 to OP2 14

E to G 829 2 96 11 OP1 to G 48 C to G 24 OP2 to G 15

TOTAL 39025 100 20366 52

Households that nominated the given tariff switch on their rebate form

Note: OP1 = Off-peak 1 tariff, OP2 = Off-peak 2 tariff

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3 BACKGROUND INFLUENCES ON ELECTRICITY CONSUMPTION

There are a number of background factors that should be taken into account when considering the

results of the HSRP.

3.1 ELECTRICITY CONSUMPTION TRENDS

Electricity consumption has been trending down over the past several years. This is a pattern found

in many parts of Australia. In its household expenditure survey, IPART found that average annual

household demand for electricity fell by around 6 per cent between 2005-06 and 2008-09 for the

Sydney metropolitan, Blue Mountains and Illawarra areas (IPART 2009).

As Figure 3-1 shows, average electricity consumption has dropped significantly since 2006 amongst

non-participant households in Ausgrid’s distribution area.

Figure 3-1 Trend in average monthly electricity consumption of non-participant households over timeframe

of HSRP

This drop in electricity consumption has probably been in part due to efficiency improvements and

also rising prices. As well as encouraging households to use less energy, rapidly rising electricity

prices have spurred efficiency improvements over the past several years, as have state and Federal

government energy efficiency programs. Figure 3-2 shows the rise in the regulated Energy Australia

residential electricity usage price over the past 10 years. The usage charge accounts for about 85 –

90% of a typical household’s electricity bill.

Jun-05 Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-12 Jun-13

14

16

18

20

22

24

26

Co

nsum

ptio

n (

kW

h/d

)

0

400

800

1,200

1,600

Th

ou

sa

nds o

f h

ouseh

old

s

Average household consumption

Number of households used to determine average consumption

21

Figure 3-2 Energy Australia regulated Domestic All Time residential electricity tariff

A consequence of this drop in electricity consumption is that there is less scope for additional energy

savings due to efficiency programs.

Table 3-1 shows the average daily electricity consumption by participant household before the HSRP

intervention according to their tariff type.

Table 3-1 Average daily participant household consumption pre-intervention by tariff

Tariff Average

kWh/day

consumption

kWh/year

Households with continuous tariff only 21.6 7,889

Households with a controlled load Off-peak tariff

Continuous component 19.3 7,049

Off-peak component 8.7 3,177

Total 28.0 10,226

The average electricity consumption of participant households prior to the intervention is shown in

Table 3-2 by the various rebate options adopted.

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12

14

16

18

20

22

24

26

28

c/kW

h

22

Table 3-2 Average pre-intervention electricity consumption by rebate item

Rebate Item Average Household Occupancy

Average Electricity Consumption

Before Rebate

kWh/day kWh/year

Gas HWS 3.19 22.7 8,291

Solar Gas HWS 3.52 24.6 8,985

Solar Electric HWS 3.01 24.7 9,021

Heat Pump HWS 3.01 24.4 8,912

3.2 THE TAKE-BACK EFFECT

A further influence when considering the results from the program is the rebound or take-back

effect. This is where households ‘take back’ some of the potential energy saving benefits from an

intervention in the form of an increased level of service. The take back effect has been most widely

studied for insulation programs. Installing insulation may mean households will take back some of

the potential energy saving in increased comfort levels rather than an energy reduction, or in some

cases even increase their energy use as they now see from benefit from using it. There is little data

available on any take-back associated with the replacement of hot water systems.

Although estimates of take-back vary widely, it is an accepted factor in retrofit programs and will

most likely have reduced the theoretical achievable savings HSRP. However, these benefits are not

easily assessable by a quantitative analysis that uses only total electricity consumption data and they

have therefore not been taken into account in this analysis.

23

4 METHODOLOGY

The primary method used for determining savings from the two HSRPs was mixed effects modelling;

an extension of conventional regression modelling that is most suited to unbalanced panel data

(combined cross-sectional and longitudinal data). Mixed modelling may also be classified as a form

of conditional demand analysis (CDA), which is widely used to model energy consumption (e.g. Vines

& Sathaye 1999, Bartels and Fiebig 2000, Isaacs et al 2006). Data supplied by OEH and Ausgrid were

first combined and processed using algorithms implemented in the Python programming language.

The modelling was then performed using the R statistical software environment. Validation of the

mixed effects modelling was also undertaken using the matched pairs means comparison (MPMC)

approach that ISF has developed specifically for this kind of billing data analysis and has previously

applied to evaluations of water and energy efficiency programs (e.g. Turner et al. 2013, Fyfe et al.

2011, Rickwood et al. 2012).

4.1 DATA PRE-PROCESSING

Data supplied by OEH data contained information on the timing and nature of participant

involvement in the rebate programs as well as some information on participant household

characteristics. Data provided by Ausgrid comprised individual metered consumption data for all

active tariffs as listed in Table 4-1. Ausgrid also supplied consumption data for approximately 1.38

million non-participating households to provide controls for the analysis. Ausgrid collects meter data

on a quarterly basis; however for the purposes of the analysis, the quarterly data were converted or

binned into monthly consumption figures by Ausgrid.

The OEH and Ausgrid participant data sets were linked using DPID and NMI numbers. Individual

participant household consumption on each tariff was then allocated to either continuous or

controlled load supply categories as per Table 4-1 in order to align with the non-participant (control)

data (which was supplied already aggregated into the two supply categories). Keeping controlled

load and continuous consumption separate helped to isolate consumption specifically related to

HWSs as controlled load meters predominantly supply HWSs (but in some instances may be

connected to pool pumps or air conditioning units). Consumption on the various tariffs was also

summed to provide a time series of total consumption for each household. Monthly consumption

figures were then converted to average daily figures by dividing by the number of metered days in

the given month (which was a separate field in the Ausgrid data). Finally, all consumption figures in

the two months immediately before and after the date of installation were removed from each

household’s record as quarterly meter reads prevent a clean demarcation of the installation in the

consumption data.

24

Table 4-1 Ausgrid electricity consumption tariffs and corresponding supply categories

Tariff Ausgrid Code Supply category

Inclining block tariff SC Continuous

Time of Use - Peak Consumption LVP

PK

Time of Use - Shoulder Consumption LVS

SH

Time of Use - Off Peak Consumption OP

Controlled load – Off peak 1 OP1 Controlled load

Controlled load – Off peak 2 OP2

4.1.1 FILTERING

Participant households were removed from the analysis under the following conditions:

Where a DPID was not given in the OEH data

Where the participant’s DPID could not be found in the Ausgrid data

Where no date of installation was recorded in the OEH data

Where there was a discrepancy in unit numbers between the OEH and Ausgrid data (this was

necessary where electricity data applied all units in a block of flats and while the rebate

applied to just one or vice versa; however if unit number only recorded in one data set, the

household was retained).

Where there was a discrepancy in street numbers between the OEH and Ausgrid data

(however if street number was supplied in only one of the two data sets, the household was

retained)

Where there were more than two discrepancies in address fields between the OEH and

Ausgrid data (to accommodate different versions of suburb names such as suburb ‘x’ and

suburb ‘x Heights’)

Where the participant has net solar generation metering

Where a participant has two differing entries in the OEH data (if the second record is a

duplicate) the duplicate (only) was removed

Some filtering was also applied to the control data set. Control households were removed from the

analysis if their consumption records contained a null entry, or if they used more than 250

kWh/hh/d in any month. There were also a number of non-residential properties that had to be

removed from the control data set.

Finally, to ensure outliers in the consumption data did not exert leverage bias on the modeling,

values that exceeded a statistically derived threshold were eliminated. The threshold was defined as

where

= the mean of the logarithmically transformed consumption data (for a given supply

category) and

= the standard deviation of the logarithmically transformed consumption data.

25

4.1.2 INTERVENTION DATE

A key explanatory variable used is the date at which the HWS was installed, which defines the before

and after program intervention periods. Data supplied by OEH provided several fields related to the

timing of rebate process.

This included the ‘Date Purchased’, ‘Date Received’ and ‘Date Installed’. It was assumed for the

purposes of this analysis that date of installation was correct and most appropriately reflected the

actual timing of the program intervention. However, in approximately 2.8% of cases the installation

date preceded the purchase date, by an average of 22 days.

4.2 MIXED EFFECTS MODELING IN R

The combination of cross-sectional participant household data and longitudinal household billing

data produces a form of panel data, with individual households constituting the subjects or panels.

Building a model to estimate savings from efficiency programs involves regressing the consumption

data (the response variable) against explanatory variables that describe individual household

consumption patterns together with their response to the program. This requires a specialised

modeling approach that is capable of accounting for innate variability between subjects (in this case

households) as well as variability over time caused by program interventions and other background

trends and shifts. Mixed modeling is essentially an extension of conventional regression modeling

that uses ‘random’ effects to help explain inter-subject (household) variability together with

conventional ‘fixed’ effects, which are equivalent to standard regression model parameters. The

general form of a mixed effects model is:

where

is the value of the response variable for the th household at time ;

are the fixed effects coefficients, which are common to all households, a subset of

which explain the effect of the efficiency program;

are the fixed effect regressors for observation in the household ;

are the random effect coefficients for household , assumed to be normally

distributed;

are the random effect regressors;

is the error for observation in group

For the purposes of electricity savings estimation, the response variable was total or controlled load

consumption, while fixed effects included HWS type (incorporating old and replacement systems),

occupancy (and its interaction with HWS type), a savings decay term, and aggregated non-

participant consumption (to control for broader trends in electricity consumption). Detailed

explanations of mixed effects modeling can be found in the extensive and diverse literature on the

topic, for example Pinheiro & Bates (2000), Verbeke & Molenberghs (2000) or West et al. (2007).

To build the model specifications for each of the rebate types a manual step-up strategy (West et al.

2007) was adopted. The strategy starts with a simple ‘unconditional’ model incorporating only the

26

most fundamental variables to explain baseline consumption and the effect of the rebate in

question, and iteratively moves towards a more sophisticated specification that accounts for other

factors that might influence consumption and savings such as occupancy, and includes additional

random effects that help explain the variance in the data. Ultimately, two criteria were applied in

selecting final model specifications:

1) The model had to make intuitive sense. In other words, model structure and coefficients had

to have a plausible link to real-world behaviour. It makes little intuitive sense, for instance,

to have a model where a variable explaining the type of new hot water system installed is

interacted with a variable for the age of the old system.

2) Model parsimony - simpler model specifications were preferred to more complex models

unless the more complex model performed significantly better than the simpler model and

also resulted in a significant change to the savings estimate.

Flow charts documenting the model specification processes for the models used to generate savings

estimates and quantify influential factors are presented in Appendix C.

Final models were fitted using the restricted maximum likelihood (REML) method, which is

considered to produce the least biased estimates of model coefficients. The inclusion of additional

variables in each step of the model specification process was tested using maximum likelihood (ML)

and REML likelihood ratio tests for fixed and random effects, respectively, to determine whether the

variable significantly improved the model fit. Confidence intervals were generated using Markov

chain Monte Carlo samples (derived from the posterior distribution of the parameters in the model)

and the highest posterior density (HPD) interval function to identify the shortest interval with a 95%

probability within the sample distribution.

4.2.1 OVERALL SAVINGS AND CONTROLLED LOAD SUBSETS

Overall program savings by HWS type were generated using the entire sample of valid participants

and total electricity consumption (sum of all tariffs) as the response variable.

Most electric HWSs tend to be connected to controlled load supply (approximately 75% of existing

HWSs amongst HSRP participant households were on an off peak tariff), which presents an

opportunity to generate savings estimates using billing data that is largely unaffected by other forms

of household consumption. As such, two additional savings analyses were performed using the

subset of participants that replaced systems on a controlled load supply with an electric system that

remained on controlled load supply or a gas system. The first quantified net electricity savings from

these off peak systems based on total consumption savings. The second considered savings on the

controlled load supply only. These analyses not only provided ‘clean’ savings estimates, but also

allowed a direct comparison with the savings estimates from an earlier study on HWS replacement

savings undertaken by Energy Australia (2009) (see Section 5.5), as well as an approximation of post-

installation contributions to continuous supply consumption from the new hot water system that

would reduce the overall saving.

The subset of participants with HWSs on controlled load supply was defined by pre- and post-

intervention tariffs using the OEH tariff data and inference from the Ausgrid data. Initially the subset

was restricted to households for which there was direct agreement between OEH and Ausgrid data.

However, the OEH data contained no information at all about post-intervention tariffs for

households that installed a heat pump. Thus to generate a controlled load savings estimate for heat

27

pumps (and a larger sample of electric-boosted solar systems), the subset was expanded to include

households that had failed to identify either the pre- or post-intervention tariff on their form by

assuming that the original tariff was retained on new electric systems. Repeating the analysis of

controlled load savings using the two subsets produced almost identical results, hence the estimates

reported in the following sections were drawn from the larger subset.

28

5 SAVINGS ESTIMATES

Table 5-1 shows the average net saving estimates for all participant households over the timeframe

of the analysis, along with the percentage of household electricity saved based on average pre-

intervention participant consumption relative to non-participant consumption. The savings

estimates are derived from the total electricity consumption of all the viable participant households

and are not based on tariff specific savings.

All results include the 95% confidence interval. The model specifications used to produce these

overall estimates are presented in Appendix C. Outputs generated by the R statistical package are

given in Appendix D.

Table 5-1 Average net electricity saving due to replacement HWS

Rebate item kWh/day kWh/year % of household

consumption

Gas HWS 7.44 ± 0.03 2,717 ± 11 29 ± 0.1

Solar Gas HWS 6.43 ± 0.09 2,348 ± 33 25 ± 0.3

Solar Electric HWS 4.11 ± 0.02 1,501 ± 7 16 ± 0.1

Heat Pump HWS 3.68 ± 0.03 1,344 ± 11 14 ± 0.1

The above results were derived from a basic model specification designed to produce overall

program savings estimates (model specification 2 in Appendix C). A second, more complex model

was formulated to quantify the effects of occupancy and time on savings (model specification 4 in

Appendix C), the results from which are presented in section 5.6. Note that the relationship between

RECs and electricity savings could not be explored as the data was incomplete. Of the 30,113

systems installed the REC data was provided for only 21,860.

Table 5-2 presents net household electricity savings for the subset of participants replacing an old

off peak system with a new gas or off peak solar or heat pump system The model specification used

for the analysis of the off peak system subset was the same used to produce the savings estimates

for total consumption (see Appendix C), with total consumption as the response variable. An

additional model using controlled load consumption as the response variable was also produced to

determine savings from replacing off peak systems.

29

Table 5-2 Average net electricity saving due to replacement of off peak HWSs (on controlled load supply).

Rebate item kWh/day kWh/year % of household

consumption

Gas HWS 8.82 ± 0.05 3223 ± 19 33 ± 0.2

Solar Gas HWS 8.46 ± 0.13 3090 ± 48 31 ± 0.5

Solar Electric HWS 4.68 ± 0.03 1710 ± 11 17 ± 0.1

Heat Pump HWS 4.37 ± 0.03 1597 ± 12 16 ± 0.1

Figure 5-1 compares the electricity savings for the overall program (based on the entire sample of

valid participants) and savings for participants replacing systems on controlled load supply by system

type. Replacement of controlled load systems produced higher net savings for all system types,

which would be expected since HWSs on continuous supply have been shown to use less electricity

on average than off peak systems (Bartels and Fiebig 2000), and therefore offer less to electricity

save. The higher consumption profile of off peak HWSs is related to the larger capacity required to

compensate for the inability to heat water during peak time.

Figure 5-1 Average overall savings and savings from replacement of off peak HWSs by system type

It is critical to note here that results for the controlled load analyses are still affected to some degree

by the uncertainty associated with electricity supply/tariffs. Due to the presence of secondary HWSs

and other appliances on off peak tariffs, we could never be 100% sure of what supply the old or new

HWS was attached to, even when OEH data appeared to agree with Ausgrid data. This is likely to

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

Heat pump Electric boosted solar Gas boosted solar Gas

Ave

rage

sav

ings

(kW

h/d

ay)

Overall savings Off peak system replacement

30

have resulted in some households that switched to a continuous supply but retained an active off

peak meter, or whose HWS was not on controlled load in the first place, being included in the

controlled load analysis. The uncertainty surrounding tariffs does not affect the overall savings

estimates, but together with the ‘dilution’ effect of other appliances, was the basis for deciding not

to report results from an analysis of savings on continuous supply. Further research is required to

understand this complexity and the implications of this for the uncertainty in the savings estimates.

5.1 HOUSEHOLDS THAT INSTALLED A GAS HOT WATER SYSTEM

Table 5-3 shows the average daily electricity savings over the timeframe of the analysis for all

households that installed a gas HWS, and for those households whose old system was on controlled

load (off peak) supply. According to the results, overall average electricity savings accruing from

switching from an electric HWS to a gas system amount to around 29% of total household demand.

Savings from off peak systems are slightly higher at 33%.

Table 5-3 Average electricity saving for households that installed a gas HWS

Saving kWh/day kWh/year % of household

consumption

All system replacements (overall saving)

Net 7.40 ± 0.03 2,717 ± 12 29 ± 0.1

Off peak system replacements (controlled load savings)

Net 8.82 ± 0.05 3,223 ± 19 33 ± 0.2

Controlled load only

9.10 ± 0.02 3,325 ± 7 100 ± 0.6

The model estimate was slightly greater than 100% due the model implicitly explaining the

variance caused by occupancy and savings decay.

Figure 5-2 shows the change in overall saving for gas systems over the timeframe of the program.

This chart is taken from the validation MPMC analysis (refer to section 6). The plot shows the strong

seasonality of savings as well as a slight but consistent decline in savings over time. The maximum

saving occurs in winter which is to be expected, as that is the time of year when typically more

energy is used for hot water.

The decay in savings could be due to a number of factors including:

A more rapid decline in general electricity consumption amongst non-participants relative to

participants.

The smaller number of participant households at the start of the program means there is

less confidence in the saving estimate at the start of the program, as can be seen from the

95% confidence interval lines.

Households that signed up early may have been more motivated by high electricity use and

therefore had the potential to save more.

The control group would contain a small percentage of households that would have

switched from electric systems to gas, solar or heat pump systems without a rebate in the

post intervention timeframe.

31

There may have been a change in the type of households that signed up over time. For

example, occupancy is the biggest factor influencing hot water use. However, an analysis

showed no difference in the occupancy of participant houses over time.

Figure 5-2 Average daily saving over time for gas HWS

Bartels and Fiebig (2000) estimated that an off-peak 1 hot water system uses approximately 3,560

kWh/year, an off-peak 2 system approximately 3,890 kWh/year and a continuous tariff system

approximately 2,700 kWh/year based on data collected in the mid-1990s. An Energy Australia (2009)

study of solar electric boost and heat pump hot water system savings found that the average

consumption of electric storage systems was approximately 3,350 kWh/year for both off-peak 1 and

off-peak 2 tariffs in 2005-2006.

It might be expected that a household switching from electric to gas water heating would save all

the electricity previously used for heating water. However, the overall savings estimate is only

slightly higher than consumption of a HWS on continuous supply despite only 31% of the water

heaters replaced in the OEH program being on a continuous tariff, while the savings from replacing

off peak systems are lower than the past estimates of consumption of those systems. These

discrepancies can be partly explained by a number of factors.

Firstly, average HWS consumption would have declined since both the previous studies were

undertaken due to measures such as minimum energy performance standards for electric storage

hot water systems and the widespread uptake of water efficient showerheads. The saving figure of

2,717 kWh/year across all tariffs and previous system types appears reasonable in this context, as

does the closeness between the savings from replacing off peak systems and the Energy Australia

(2009) off peak system consumption figure relative to the difference between savings and the

corresponding Bartels and Fiebig (2000) figures.

32

Secondly, not all of the households that switched to gas hot water would have saved all the

electricity previously used for water heating. A large number of households switched to gas

instantaneous units which use some continuous tariff electricity both in standby mode and also

when heating water. A typical instantaneous system uses about 5-10 W in standby and about 50 W

when operating, a total of about 50 - 100 kWh/year. For off peak systems replaced with gas storage

and instantaneous systems, the controlled load saving was 9.1 kWh/day, which amounts to 100% of

what demand would otherwise be without replacing the system. This equates to 3,325 kWh per

year, which is very close to the annual electricity consumption found by Energy Australia (2009) for

off-peak electric storage systems of 3,350 kWh/year. Moreover, the difference between the net and

controlled load savings from replacing off peak systems (111 kWh/year) may be interpreted as the

average electricity consumption associated with replacement gas systems and agrees with the

engineering estimate given above.

5.2 HOUSEHOLDS THAT INSTALLED A GAS-BOOSTED SOLAR HWS

Table 5-4 shows the average electricity savings over the timeframe of the analysis for all households

that installed a gas-boosted solar HWS and for those that replaced an off peak system with gas-

boosted solar. As for gas hot water systems, there was a small decline in the saving over the

timeframe of the analysis (refer to Appendix E).

Table 5-4 Average electricity saving for households that installed a gas-boosted solar HWS


consumption


Net 6.43 ± 0.09 2,348 ± 33 25 ± 0.3


Net 8.46 ± 0.13 3090 ± 48 31 ± 0.5


8.98 ± 0.05 3,281 ± 18 100 ± 0.6

The model estimate was actually slightly greater than 100% due the model

implicitly explaining the variance caused by occupancy and savings decay.

At 6.43 kWh/day or 25% of household demand, overall savings from solar gas systems are less than

those for conventional gas systems (7.40 kWh/day). This is likely to be due to the additional power

consumption of pumps and controllers associated with split solar HWSs. Split systems use small

electric pumps to circulate water between the tank (at ground level) and the panels on the roof. A

100-W pump operating for four hours per day on average with a controller that uses 5W would

together use about 0.5 kWh/day. The control panel that monitors water temperatures and operates

the pump would also apply a small, but continuous draw on the power supply. In addition, some in-

tank gas boosters on thermosiphon and split systems use electronic ignition to start the burner.

These factors would account for some of the difference between the conventional and solar gas

systems.

The analysis of savings from replacing off peak systems detected a saving for gas-boosted solar

systems of 8.46 kWh/day, which equates to approximately 3,090 kWh/year or 31% of total

33

household consumption. The controlled load saving from off peak systems was slightly higher at 8.98

kWh/day, confirming that solar gas systems are using additional continuous tariff electricity and

thereby reducing the net saving. The difference between the net and controlled load savings for

replacing off peak systems may be considered a measure of the energy consumption of additional

electrical and electronic componentry, which at 0.52 kWh/day (190 kWh/year) is very similar to the

engineering approximation of pump consumption. Importantly, and as would be expected, the

difference is higher than the corresponding difference between net and controlled load savings for

conventional gas systems, which do not incorporate electrical pumps.

5.3 HOUSEHOLDS THAT INSTALLED ELECTRIC-BOOSTED SOLAR HWS

Table 5-5 shows the average daily saving in total electricity consumption over the timeframe of the

analysis for all households that installed an electric-boosted solar HWS and those that replaced a

conventional off peak system with an electric-boosted solar system.

Table 5-5 Average saving in total electricity per household that installed an electric-boosted solar HWS


consumption


Net 4.11 ± 0.02 1,500 ± 9 16 ± 0.1


Net 4.68 ± 0.03 1,710 ± 11 17 ± 0.1


4.53 ± 0.01 1,651 ± 4 53 ± 0.1

It can be seen from Figure 5-3 that the savings are much less uniform than for gas systems and have

declined significantly over time. The maximum saving occurs in summer rather than in winter, as

summer is when the solar contribution to water heating is greatest and therefore results in the

highest savings. Solar radiation also varies significantly, resulting in a more irregular saving pattern.

The larger decline in the saving over time for electric-boosted solar systems compared with gas

systems can be partly explained from an analysis of solar radiation data from the Bureau of

Meteorology. Figure 5-4 shows that the average daily solar radiation in the Ausgrid area has trended

downwards between 2009 and 2012. This would reduce the solar contribution to hot water heating

and therefore also the electricity savings.

34

Figure 5-3 Average daily saving over time for electric-boosted solar HWS

Figure 5-4 Variation in average monthly solar exposure for Sydney 2009-2012

Source: Bureau of Meteorology solar exposure data for Sydney (Observatory Hill), Newcastle (Nobby’s Head) and Maitland

(Visitors’ Centre).

The analysis of replacement of off peak systems produced very similar net and controlled load saving

estimates of 4.68 kWh/day and 4.53 kWh/day, respectively. As for gas-boosted solar systems, split

5

10

15

20

25

30

Jan

-09

Mar

-09

May

-09

Jul-

09

Sep

-09

No

v-0

9

Jan

-10

Mar

-10

May

-10

Jul-

10

Sep

-10

No

v-1

0

Jan

-11

Mar

-11

May

-11

Jul-

11

Sep

-11

No

v-1

1

Jan

-12

Mar

-12

May

-12

Jul-

12

Sep

-12

No

v-1

2

Sola

r ra

dia

tio

n (

MJ/

m2)

Sydney Newcastle Maitland

35

solar electric systems will use some electricity for pumping and controls which should cause

controlled load savings to be higher than net savings. The results here contradict this expectation,

although the apparent difference is not substantially greater than the confidence bounds of the

savings estimates, and may in part be due to misreporting of tariffs.

5.4 HOUSEHOLDS THAT INSTALLED A HEAT PUMP HWS

Table 5-6 shows the average daily savings in electricity consumption over the timeframe of the

analysis for households that installed a heat pump HWS. As mentioned earlier, data supplied by OEH

contained no information on post-installation tariffs for heat pumps. This required that the subset

used in the controlled load analysis rely on inference from the Ausgrid data and, where the post-

intervention tariff was not clear from the Ausgrid data, the assumption that the original HWS tariff

was retained. The heat pump subset so defined could well include households with HWSs falsely

identified to have stayed on controlled load when they actually switched their new HWS to

continuous supply while retaining a secondary HWS or another appliance on the controlled load

meter. Indeed according to this approach more than 2,000 out of a total of nearly 13,000

participants (15%) would theoretically have stayed on the off-peak 1 tariff, when heat pumps are

generally advised to be connected to an off-peak 2 or continuous supply to work effectively.

While repeating the analysis on a more tightly defined subset verified the savings estimates for the

other HWS types, with no OEH data on post-intervention tariffs, the heat pump estimate could not

be verified in this manner. However, the net and controlled load estimates for savings from

replacing off peak systems are very similar, which would suggest that the large majority of the off

peak subset were accurately identified. Nevertheless, the estimates for savings from heat pumps

replacing conventional off peak systems must be treated with caution and additional analysis would

be required to properly gauge the accuracy of the estimates.

Table 5-6 Average electricity saving for households that installed heat pump HWS


consumption


Net 3.68 ± 0.03 1,347 ± 10 14 ± 0.1


Net 4.37 ± 0.03 1,597 ± 12 16 ± 0.1


4.32 ± 0.01 1,578 ± 5 51 ± 0.1

5.5 COMPARISON WITH ENERGY AUSTRALIA FINDINGS FOR HWS REPLACEMENT

Energy Australia (2009) undertook an analysis of the savings from the installation of heat pump and

electric-boosted solar hot water systems installed by some of their customers between December

2006 and June 2008. A total of 136 electric-boosted solar and 17 heat pump systems were analysed.

For ease of analysis Energy Australia (2009) only considered households that remained on an off-

peak tariff as the consumption is separately metered. The analysis did not separate OP1 and OP2

36

tariffs. Nor did it use a control group, relying instead on a simple before and after analysis of

consumption, and is therefore fundamentally different to the analysis approach used in this report,

and is not directly comparable. Simple before and after savings analyses tend to over- or under-

estimate savings as they do take into account background changes in consumption.

The Energy Australia study found that heat pumps saved an average of approximately 1,500

kWh/year, which agrees very closely with the controlled load saving of 1,578 kWh/year determined

in this analysis. Electric-boosted solar systems were found by Energy Australia to save approximately

2,200 kWh/year. The present study produced a corresponding estimate of 1653 kWh/year for

controlled load savings. However, there are several factors that could account for some of this

difference.

Firstly, the annual average solar radiation over the post installation 12 month billing period used for

the EA study was significantly higher than in the pre installation 12 month billing period, 18.4 MJ/m2

compared to 16.4 MJ/m2, which would have increased the solar savings due to a 12% higher solar

contribution.

Secondly, the EA study households also had lower average occupancy than the OEH participants at

2.7 compared to 3.1. Smaller households generally mean larger savings with solar systems as less

boosting is required to meet the additional demand. The EA study found that the households that

had an average occupancy of 2.0 saved more electricity (2,658 kWh/year) while those with an

average occupancy of 3.8 actually saved less (1,115 kWh/year). The impact of occupancy on savings

from the HSRP is discussed in the following section.

5.6 THE EFFECT OF OCCUPANCY AND TIME ON HWS SAVINGS

Figure 5-5 and Figure 5-6 show the effect of occupancy and time on the saving for each hot water

system type. For all systems except solar electric boost, the saving increases with occupancy. For all

gas systems, the increase with occupancy is explained by the fact that hot water energy use is

strongly correlated with the number of people in a household, and almost all the electricity used for

hot water is saved by installing a gas system. For solar electric systems, more people in a household

means that more electric boosting will be required to meet the hot water demand, hence while the

saving increases between 1 and 2 person households, it tapers off again when occupancy is higher

than 3. This agrees with the finding of the Energy Australia (2009) study.

There was a noticeable decline in savings over time with the exception of gas instantaneous and

storage systems which demonstrated only a very small decline in savings over time. Decay in savings

is not surprising given the drop in residential electricity consumption shown in Figure 3-1. It is likely

that part of this reduction is due to the decline in electricity demand for hot water in non-participant

households due to the implementation of minimum energy performance standards for electric

storage hot water systems and the presumably small voluntary (non-rebated) uptake of solar, gas

and heat pump systems amongst non-participants. It is also possible that the take up of water

efficient showerheads may have been greater in the non-participant control group than in the

participant group.

It was also observed that a number of participant households who switched from controlled load to

gas started to consume some controlled load electricity at varying times after the new HWS was

installed. The reasons for this are not clear, but may be due to connecting some other item such as a

second HWS or a pool filter pump to the controlled load supply. Determining the significance of this

37

change on the decline in savings is not within the scope of this study as it would require a different

type of analysis on both the participant and control groups.

Figure 5-5 Saving by HWS and household occupancy 3 months after installation

Figure 5-6 Saving by HWS and household occupancy 24 months after installation

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

1 2 3-5 6+

Ave

rage

sav

ings

(kW

h/d

)

Occupancy

3 months after installation


0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

1 2 3-5 6+

Ave

rage

sav

ings

(kW

h/d

)

Occupancy

24 months after installation


38

6 VALIDATION OF MIXED MODELLING RESULTS USING MPMC

In past efficiency program evaluation studies, ISF has applied an alternative methodology to

estimating savings that is founded upon overcoming common data limitations associated with such

programs. The matched pairs means comparison method (MPMC) requires only billing data and

intervention dates to generate savings estimates. To control for external factors that influence

household consumption it draws on the pool of non-participants to identify control households that

are paired with participants according to their pre-intervention consumption patterns. The MPMC

method was applied in this study both as a preliminary analysis and as a means of validating the

results produced by the mixed modelling approach.

No two techniques are likely to produce identical results, as they will rely on different underlying

mathematical assumptions. Nevertheless, the estimates of savings from the two techniques agree

fairly closely. It can be seen from Table 6-1 that although there are slight differences in the estimates

for overall net savings, they are similar in magnitude and relativity, providing confidence in the

mixed modelling results. In addition, the time series of savings produced by the MPMC exhibited

decay trends that agreed closely with the decay variable coefficients derived through the mixed

modelling approach. The basic MPMC methodology and the outputs from the analysis are presented

in Appendix E.

Table 6-1 Comparison of regression and MPMC saving results

Mixed modeling estimate

MPMC

estimate

Program measure kWh/day kWh/day

Gas HWS 7.44 ± 0.03 7.16 ±0.04

Solar Gas HWS 6.43 ± 0.09 5.61 ±0.12

Solar Electric HWS 4.11 ± 0.02 3.69 ±0.03

Heat Pump HWS 3.69 ± 0.03 3.15 ±0.03

39

7 COMMENTARY ON HSRP DATA

The household data collected by OEH via the rebate application forms provided a sound platform for

the mixed modeling CDA approach used to estimate savings. However, the analysis could have been

further enriched by the collection of a handful of additional pieces of information, while the useable

samples could potentially have been larger and the reliability of the estimates been stronger with a

number of simple quality control measures.

For the HWS savings analysis it would have been of value to know whether the solar systems

installed were split systems or roof top thermosiphon systems. This may have allowed an estimate of

how much continuous tariff electricity is used by pumps and controls in split systems. It would also

have been useful to have had the data on whether a gas system was storage or instantaneous made

available to this analysis (the rebate form did in fact include fields for this distinction) as this could

allow an analysis of the electricity consumed by mains powered instantaneous systems. Inclusion on

the rebate form of a data field describing the type of showerheads installed in the main bathroom(s)

would have allowed some exploration of their impact on the savings achieved and also a comparison

with the broader non-participant ownership to inform the understanding of the savings.

For future reference in relation to data quality, data on tariffs may have been more reliable if the

installer was required to record and sign off on the before and after electricity tariffs, as they should

have a better understanding than the householder based on the findings in this study. The installer

could also have checked whether secondary HWSs or other appliances were connected to off peak

meters. In addition, it could have been suggested on the rebate form that a recent electricity bill be

used to help identify existing tariffs. Finally, in order to maximise sample numbers for billing data

analysis by simplifying the process of matching participants to their consumption data records, NMI

could have been made a compulsory field on the rebate form.

40

8 CHANGES TO PARTICIPANTS’ ELECTRICITY BILLS

Changes to electricity bills are based on the current Energy Australia regulated electricity prices

shown in Table 8-1.

Table 8-1 Energy Australia regulated electricity prices 2012-2013, c/kWh

Continuous OP1 OP2

First 1000 kWh/quarter

(Less than 11kWh/day)

26.84 11.11 14.63

Next 1000 kWh/ quarter

(Between 11-22 kWh/day)

28.05

Remaining usage

(more than 22 kWh/day)

37.73

On average, the participant households with a continuous only supply prior to installing a new hot

water system used 21.6 kWh/day, and would therefore fall within the second block of the tariff

structure for the purpose of estimating bill savings.

Table 8-2 shows the average savings made by replacing a hot water system when switching from off-

peak or continuous tariffs electric storage systems. As it was not possible to isolate the average

usage or savings for electric HWSs on continuous supply from the data4, the same (overall) saving

has been assumed for continuous and off peak systems. As such, estimated bill savings do not

account for details such as the price differential between electricity saved an on off peak tariff and

additional energy consumed on the continuous tariff when replacing an off peak system with a

conventional or solar gas HWS, making the estimates of bill savings very coarse. Similarly, estimating

savings on time-of-use tariffs was not feasible as there was no means of determining when

electricity for water heating is used. An arbitrary allocation of savings to different times of the day

would be highly likely to produce misleading results. It must also be noted that the savings from

switching to gas are electricity savings only, and would be offset by gas costs. No gas consumption

data was provided to be able to calculate overall energy bill savings.

4 Attempts to do so produced contradictory and unreliable estimates, mainly due to the poor reporting

of tariffs

41

Table 8-2 Average annual electricity bill saving for different hot water systems

Replacement HWS Original tariff

Off-peak 1 Off-peak 2 Continuous

Gas - instantaneous or storage* 302 398 729

Solar Gas boost* 261 344 660

Solar Electric OP1 172 282 639

Solar Electric OP2 118 227 586

Solar Electric continuous -72 38 403

Heat Pump OP1 149 251 572

Heat Pump OP2 95 197 525

Heat Pump Continuous -62 7 361

*Additional gas costs not included

42

9 CONCLUSIONS AND RECOMMENDATIONS

Overall the electricity savings from hot water systems were in close alignment with what would be

expected from simple ‘engineering’ calculations and previous studies. Validation using MPMC

indicated the magnitude and relativity of savings estimates are reliable.

Households that switched to gas systems showed the highest level of electricity savings as would be

expected. No analysis was undertaken of gas usage. Heat pumps showed the lowest level of savings.

Electric boosted solar had a negative relationship with the occupancy level due to greater boosting

required in larger households.

Households that switched to gas-boosted solar systems saved less electricity overall on average

compared to those who switched to gas storage or instantaneous systems. This would likely be due

to the monitoring and pumping ignition requirements of split solar systems. Unfortunately the OEH

rebate form data did not indicate if a system was a split or all-in-one system so this could not be

investigated in detail.

Amongst households that had been on a controlled load supply (off peak) HWS and switched to a gas

supply, net savings were less than the savings on the controlled load supply only. This would in part

be due to the electricity use of gas instantaneous systems that use electronic ignition. The data

provided by OEH did not indicate if a gas system was storage or instantaneous (although this data

was collected on the rebate application form) so this could not be investigated in detail.

Many households had a poor understanding of which tariff(s) they were using for their electricity

supply. For example, approximately half of those who remained on a continuous tariff after the

installation of a new hot water system reported that they had changed to an off-peak tariff. This

made it difficult to undertake an analysis of tariff based savings and creates unquantifiable

uncertainty in the controlled load savings estimates.

Recommendation: In future programs of this type the system installer should be required to verify

the previous and new tariffs used for the HWS

An analysis of controlled load to gas system switches found many instances where controlled load

consumption did not drop to zero after the installation or started up again several months after the

installation. In some cases the increase is large. Overall the post-intervention controlled load

consumption amongst households that supposedly discontinued using an off peak tariff averaged 1.5

kWh/day, and indicates that there are other appliances such as pool filter pumps or possibly

secondary HWSs connected to controlled load supply. The impact of this on the estimated saving is

not known and would require further analysis to investigate. The same issue could also contribute to

the observed decline in savings over time. This would require further analysis beyond the scope of

this study.

Recommendation: Future programs related to hot water electricity consumption should require

the installer to investigate if there is any other equipment connected to the controlled load supply.

Recommendation: Undertake a follow-up survey and/or further analysis of the data to determine

the significance of the post installation increase in controlled load consumption.

Decay in savings over time was observed for all the rebate items. This could be due to several

factors:

43

‘efficiency creep’ amongst non-participant households that is not reflected amongst

participant households e.g. new electric hot water systems being more efficient due to

MEPS;

‘voluntary’ system changes amongst non-participants, such as installing solar water heaters

without a rebate.

Random follow-up audits could be considered to check the overall standard of installations to

determine whether workmanship quality plays a role in the savings achieved. For example, a survey

undertaken in 2000 by the ISF of solar hot water systems installed in a Sydney LGA found that two of

the 15 systems examined had been incorrectly installed, resulting in very low or possibly no

electricity savings.

Recommendation: Subsidised installations of large energy efficiency measures should be backed

up by a small number of random audits some months post the installation to confirm the correct

installation of equipment. This could also be used to ascertain if any new equipment has been

connected to the controlled load supply.

Finally, to improve the robustness of ex-post evaluation, a greater emphasis could be placed on

reliable data collection, requiring installers to assist with particular pieces of information that the

householder may not be familiar with (in particular electricity tariffs) and householders to provide

their NMI number from their electricity bill to assist with data matching with electricity distributors.

Recommendation: Consider provision of NMI number being made a pre-requisite to receiving a

rebate or subsidy under future energy efficiency programs.

44

10 REFERENCES

Bartels, R. and Fiebig, D. 2000. Residential end-use electricity demand: Results from a designed

experiment. Energy Journal, 21(2):51–81.

Energy Australia (2009). Efficiency of electrically boosted solar and heat pump hot water systems.

Prepared for DEWHA.

Fyfe, J., May, D. & Turner, A., 2010. Techniques for estimating water saved through demand

management and restrictions. In Integrated resource planning for urban water—resource papers.

Waterlines. Canberra: National Water Commission, pp. 145-194.

Fyfe J, Mohr S, May D & Rickwood P 2011b, Statistical Evaluation of Water, Electricity and

Greenhouse Gas Savings from the Think Water, Act Water Residential Efficiency Programs, prepared

for the ACT Environment and Sustainable Development Directorate by the Institute for Sustainable

Futures, UTS, Sydney.

IPART 2006. Residential Energy use in Sydney, the Blue Mountains and Illawarra. Research Paper

RP28, Independent Pricing and Regulatory Tribunal of NSW, Sydney ISBN 9781921328008.

IPART, 2009. Review of regulated retail tariffs and charges for electricity 2010-2013, Independent

Pricing and Regulatory Tribunal of NSW, Sydney.

IPART, 2012. Changes in regulated electricity retail prices from 1 July 2012, Independent Pricing and

Regulatory Tribunal of NSW, Sydney.

Milne G., Boardman B., 2000. Making cold homes warmer: The effect of energy efficiency

improvements in low income households, Energy Policy, Volume 28, Number 6, pp. 411-424.

Pinheiro, J. & Bates, D., 2000. Linear mixed-effects models: basic concepts and examples, New York:

Springer.

Rickwood, P., 2009 Residential Operational Energy Use. Urban Policy and Research, 27(2).

Rickwood, P., Mohr, S., Nguyen, M., Milne, G., Fyfe, J. 2012, Evaluation of the home power savings

program – Phase 2, prepared for the NSW Office of Environment and Heritage by the Institute for

Sustainable Futures, UTS.

Turner, A. et al., 2013. ARE WE THERE YET ? The importance of evaluating efficiency programs.

Water, May, pp.2–7.

Verbeke, G. & Molenberghs, G., 2000. Linear mixed models for longitudinal data, New York:

Springer-Verlag.

Vines, E. and J. Sathaye, 1999. Guidelines for the Monitoring, Evaluation, Reporting, Verification, and

Certification of Energy-Efficiency Projects for Climate Change Mitigation. Berkely, CA.

West, B., Welch, K. & Galecki, A., 2007. Linear mixed models: a practical guide using statistical

software, Boca Raton: Taylor & Francis Group.

http://www.ingentaconnect.com/content/els/03014215;jsessionid=mggi8qmphc0o.alexandra

45

APPENDIX A: Breakdown of hot water systems installed by manufacturer and type

Brand (from OEH data) Gas Heat Pump Solar - Electric Boosted

Solar - Gas Boosted

Grand Total

AAE Solar 7 295 11 313

ABTT 3 3

Apricus Australia 2 889 42 933

AquaMax 6 82 19 107

Aqua-Max 25 25

Aquamax (External) 118 118

Aquamax 135 (External) 7 7

Aquamax 300 (External) 6 6

Aquamax Continuum 20 & 24 (External) 2 2

Atlas Trading 1 1

Beasley 136 63 199

Bosch 1 1 3 1 6

Bosch 10H External Models 3 3

Bosch 16H External Models 29 29

Bosch Commercial Internal/External 3 3

Bosch Highflow 70 Series External 677 677

Bosch Highflow External 6 6

Bosch Internal Instantaneous Water Heaters 1 1

Bosch Internal Models 1 1

Bosch13H External Models 3 3

Chromagen 445 556 24 1025

Chromogen Models 21 21

Conergy 9 628 7 644

Douglas Solar 1 1

Dux 2045 2819 122 4986

Dux Endurance 20i (Internal Water Heaters 1 1

46


Solar - Gas Boosted

Grand Total

Dux Endurance External Mains Pressure Water Heater 6 6

Dux Endurance Instantaneous Water Heater 80 80

Dux Prodigy Storage Water Heater 17 17

Easy Being Green 4 4

ECCO Solar 98 98

Ecosmart 58 1394 70 1522

Edwards 30 1613 68 1711

Edwards Comfort Models 1 1

Endless Solar 1 269 41 311

Everhot 3 62 4 2 71

Everhot Models 124 124

GREENLAND SYSTEMS 1 1

Hills Solar 55 3 58

Icon Solar 1 1

Instantaneous Water Heater Model 1 1

Invert Energy 4 4

J V Solar 3 3

Natural Heat 2 2

Neopower 1 1

Paloma Models 1 1

Quantum 502 5 507

Radiant 3 3

Rheem 82 7116 949 89 8236

Rheem Integrity Models 923 923

Rheem Optima 850 Series 880 880

Rheem Rheemglas 350 Series 14 14

Rinnai 1 18 12 31

Rinnai V Series 282 282

Rinnai Beasley 4 4 8

47


Solar - Gas Boosted

Grand Total

Rinnai Demand Duo 1 1

Rinnai Eviro Smart (External) 1 1

Rinnai External (HD250e) 1 1

Rinnai External (Infinity 32) 142 142

Rinnai External Models 2259 2259

Rinnai Models 2 2

Rinnai Prestige 143 42 185

Rinnai Roofmaster 2 2

Rinnai Sunmaster 6 581 557 1144

Rinnai V Series 9 9

Run On Sun Australia 1 1 2

Saxon 2017 20 2037

Siddons Solarstream 74 74

Sidek Solar 1 7 8

SKYSOLAR 28 2 30

Solahart 78 5098 79 5255

Solahart Models 2 2

Solar Lord 45 3 48

SolarArk 45 1 46

Solitaire 2 10 1 13

Stiebel Eltron 459 4 1 464

SUN RAY 1 1

Sunheat 2 2

Sunrain 10 1 11

Suntrap 2 71 8 81

Thermapower 1 1

THERMOTEC 2 2

Velux-Dux 1 1 2

Vulcan 1 5 6

48


Solar - Gas Boosted

Grand Total

Unspecified 3172 3172

Grand Total 8912 12923 15906 1284 39025

49

APPENDIX B: Detailed tariff analysis

Table of tariff change for participants that obtained a gas instantaneous or storage HWS

Ausgrid data

OEH data OP1 to G OP2 to G CTS to G E to G Total

OP1 to G 3213 554 354 53 4174

OP2 to G 328 481 57 31 897

CTS to G 479 223 2389 9 3100

E to G 362 112 181 86 741

Total 4382 1370 2981 179 8912

Table of tariff change for participants that obtained a solar gas boost HWS

Ausgrid data

OEH Data OP1 to G OP2 to G CTS to G E to G Total

OP1 to G 502 92 76 16 686

OP2 to G 51 82 18 3 154

CTS to G 46 23 286 1 356

E to G 39 14 25 10 88

Total 638 211 405 30 1284

50

Table of tariff change for participants that obtained a heat pump HWS,

(note that the tariff the participants went to was not stated, hence OEH data assumed no tariff change)

Ausgrid data

OEH data OP1 to OP1

OP1 to OP2

OP1 to CTS

OP1 to E

OP2 to OP1

OP2 to OP2

OP2 to CTS

OP2 to E

CTS to OP1

CTS to OP2

CTS to CTS

CTS to E

E to OP1

E to OP2

E to CTS

E to E

Total

OP1 to OP1 2058 2095 1082 258 11 389 128 1 76 43 254 28

15 3 6441

OP2 to OP2 389 696 166 46 26 1256 177 61 24 67 144 12

30 20 23 3137

CTS to CTS

116 280 4 1

88

331 224 1524 129

1

2698

E to E 172 102 89 6 4 97 22

12 6 86 4

9 16 22 647

Total 2619 3009 1617 314 42 1742 415 62 443 340 2008 173 0 39 52 48 12923

51

Table of tariff change for participants that obtained a solar electric boost HWS

Ausgrid data

OEH data OP1 to OP1

OP1 to OP2

OP1 to CTS

OP1 to E

OP2 to OP1

OP2 to OP2

OP2 to CTS

OP2 to E

CTS to OP1

CTS to OP2

CTS to CTS

CTS to E

E to OP1

E to OP2

E to CTS

E to E

Total

OP1 to OP1 5030 665 690 118 132 783 66

285 56 778 28 28 2 13 2 8676

OP1 to OP2 442 236 61 3 16 113 5 1 15 10 69

1 2 3 977

OP1 to CTS

44 449

2

66

3 1 89

1

655

OP2 to OP1 187 33 21 6 75 286 35

18 22 56 3 1 3 1 2 749

OP2 to OP2 382 146 52 2 62 767 77 29 25 63 177 6

17 9

1814

OP2 to CTS

5 59

8

98

2 24

1 4

201

CTS to OP1

11 11

4

1

251 91 404 12

1

786

CTS to OP2

12 3 1

4

42 52 95 9

218

CTS to CTS

14 68

3

33

46 37 1495 18

2

1716

E to E 44 6 7

2 10 2 1 6

27 2

3 4 114

Total 6085 1172 1421 130 304 1959 387 31 691 334 3214 78 29 24 36 11 15906

52

APPENDIX C: Mixed effects model specifications

The step-up model specification process for the HWs rebate model based on total electricity

consumption is pictured in Figure C-1. Each number represents a permutation of the model equation

5 in section 4.2 as detailed in Table C-1. The model variables are explained in Table C-2.

Table C-1 Model equations tested in the HWS model step-up specification process

Model Equation Explanation

1 Rudimentary marginal model for savings by

HWS type.

1a Include random effects to explain variation

inherent to participant households (intercept

term plus a term related to average non-

participant demand)

1b As above Assuming uncorrelated household random

effects

2

Population-wide fixed effect for non-

participant demand (uncorrelated random

effects)

3

Add a fixed effect to explain overall decay in

savings

3a

Replace overall decay term with interaction

term making decay specific to system type

4

Add an interaction term to explain the effect

of occupancy on savings (by HWS type)

5

Incorporate a seasonal component to the

random effect related to average non-

participant demand

53

Table C-2 Variables used in the HWS mixed modeling for total electricity consumption

Variable Description Type Levels Notes

Total electricity

consumption for

household at time

(kWh/d)

Continuous NA Sum of all tariffs

HWS type by

household and time

Categorical Electric (pre-

intervention only);

gas; gas-boosted

solar; electric-

boosted solar; heat

pump

Fundamental program effect

variable – the corresponding

regression coefficients for each

level provide the savings estimate

Average non-

participant

consumption at time

Continuous NA Accounts background for trends

and shifts in electricity

consumption

Month relative to

new HWS

installation (integer

count)

Continuous Simple time elapse variable

quantifies the decay in savings over

time

Occupancy in

participant

household

Categorical 1; 2; 3-5; 6+;

unknown

Using a categorical variable instead

of a continuous specification

prevented data loss associated

with missing occupancy data and

simplified the explanation of the

model outputs

Season during which

consumption record

for household

was generated

Categorical Summer; autumn;

winter; spring

Provides greater resolution to

household random effects

54

Figure C-1 Mixed model specification flow chart. Numbers correspond to models listed in Table A-1. Red

lines indicate REML log likelihood ratio tests, blue maximum likelihood log likelihood ratio tests, and green

model outputs.

The Model 2 specification in Table C-1was also adopted for analysing total savings from the subset of

participants with HWSs on controlled load supply. The same model specification was used to analyse

savings on the actual controlled load supply for the same subset, but using consumption on the

controlled load supply as the response variable (as opposed to total consumption) and the

normalised non-participant control data:

Where

Controlled load supply electricity consumption for household at time ;

Average non-participant controlled load supply consumption at time (kWh/d).

1

1a1b

2

33a

4 5

Overall savings

estimates

Savings decay and occupancy

effects

Significant increase in time to solution formarginal gain in explanation of variance andnegligible change in savings estimate

Significant improvement to model fit

Significant increase in time to solution formarginal gain in explanation of variance andnegligible change in savings estimate

55

APPENDIX D: Mixed modelling outputs (from R)

OVERALL SAVINGS MODEL (MODEL SPECIFICATION 2)

Linear mixed model fit by REML

Formula: tot ~ (1 | id) + (0 + nct | id) + nct + system

Data: hws

AIC BIC logLik deviance REMLdev

18812586 18812702 -9406284 18812532 18812568

Random effects:

Groups Name Variance Std.Dev.

id (Intercept) 124.3693 11.1521

id nct 1.3197 1.1488

Residual

38.4918 6.2042

Number of obs: 2850854, groups: id, 38490

Fixed effects:

Estimate

Standard error t value

(Intercept) 25.93313 0.057111 454.1

nct 1.263098 0.006094 207.3

Gas -7.40133 0.017758 -416.8

Heat pump -3.69525 0.01383 -267.2

Electric boosted solar -4.1122 0.012699 -323.8

Gas boosted solar -6.44461 0.045314 -142

Correlation of Fixed Effects:

(Intr) nct systmG systHP syS-EB

nct -0.005 systemGas -0.032 0.04

systemHtPmp -0.039 0.049 0.003 systmSlr-EB -0.041 0.054 0.003 0.004

systmSlr-GB -0.012 0.016 0.001 0.001 0.001

Confidence intervals by MCMCsamp:

Fixed effects

Estimate

MCMC mean

HPD95 lower

HPD95 upper pMCMC Pr(>|t|)

56

(Intercept) 25.933 25.936 25.879 25.992 0.0001 0

nct 1.263 1.264 1.252 1.275 0.0001 0

Gas -7.401 -7.438 -7.471 -7.4 0.0001 0

Heat pump -3.695 -3.688 -3.715 -3.66 0.0001 0

Electric boosted solar -4.112 -4.107 -4.131 -4.08 0.0001 0

Gas boosted solar -6.445 -6.428 -6.517 -6.338 0.0001 0

Random effects

Groups Name Std.Dev. MCMC median

MCMC mean

HPD95 lower

HPD95 upper

id (Intercept) 11.1521 5.4903 5.5096 5.4698 5.5104

id nct 1.1488 1.1254 1.1255 1.1172 1.1341

Residual

6.2042 6.3416 6.3409 6.3361 6.3475

MODEL INCORPORATING OCCUPANCY AND SAVINGS DECAY (MODEL SPECIFICATION 4)


Formula: tot ~ (1 | id) + (0 + nct | id) + nct + system + system * occ + months.after.int.date:system

Data: hws


18800684 18801121 -

9400308 18800459 18800616

Random effects:


id (Intercept) 103.3225 10.1648

id nct 1.3145 1.1465

Residual

38.4252 6.1988

Number of obs: 2850854, groups: id, 38490 Fixed effects:

Estimate

Std. Error t value

(Intercept) 15.69159 0.192775 81.4

nct 1.276403 0.006091 209.57

systemGas -6.26257 0.075239 -83.24

systemHeat Pump -3.55013 0.051323 -69.17

systemSolar - Electric Boosted -4.20164 0.048384 -86.84

systemSolar - Gas Boosted -6.49688 0.295066 -22.02

occ2 6.581685 0.213144 30.88

occ3-5 13.9616 0.20645 67.63

occ6+ 20.05263 0.346473 57.88

occUnknown 10.304 0.267768 38.48

systemGas:occ2 -0.67483 0.079653 -8.47

57

systemHeat Pump:occ2 -0.58228 0.053324 -10.92

systemSolar - Electric Boosted:occ2 -0.85606 0.049961 -17.13

systemSolar - Gas Boosted:occ2 -0.66184 0.304815 -2.17

systemGas:occ3-5 -1.86876 0.076164 -24.54

systemHeat Pump:occ3-5 -0.99773 0.051766 -19.27

systemSolar - Electric Boosted:occ3-5 -0.4283 0.048885 -8.76

systemSolar - Gas Boosted:occ3-5 -0.81125 0.29536 -2.75

systemGas:occ6+ -3.61939 0.122174 -29.62

systemHeat Pump:occ6+ -0.93602 0.091999 -10.17

systemSolar - Electric Boosted:occ6+ -0.05909 0.082019 -0.72

systemSolar - Gas Boosted:occ6+ -2.89156 0.365582 -7.91

systemGas:occUnknown -1.45109 0.093917 -15.45

systemHeat Pump:occUnknown -0.74068 0.071802 -10.32

systemSolar - Electric Boosted:occUnknown 0.445831 0.063904 6.98

systemSolar - Gas Boosted:occUnknown -0.25784 0.342527 -0.75

systemELEC:months.after.int.date 0.009325 0.000372 25.08

systemGas:months.after.int.date 0.004752 0.001014 4.69

systemHeat Pump:months.after.int.date 0.02061 0.000845 24.39

systemSolar - Electric Boosted:months.after.int.date 0.018323 0.000784 23.39

systemSolar - Gas Boosted:months.after.int.date 0.034265 0.002881 11.89


(Intr) nct systmG systHP syS-EB syS-GB occ2 occ3-5 occ6+ occUnk sysG:2 syHP:2

nct 0

systemGas -0.035 0

systemHtPmp -0.048 0 0.021

systmSlr-EB -0.051 0 0.023 0.032

systmSlr-GB -0.009 0 0.004 0.005 0.006

occ2 -0.903 0 0.027 0.036 0.039 0.007

occ3-5 -0.932 0 0.028 0.038 0.04 0.007 0.843

occ6+ -0.555 0 0.017 0.022 0.024 0.004 0.502 0.518

occUnknown -0.718 0 0.021 0.029 0.03 0.005 0.65 0.671 0.4

systmGs:cc2 0.028 0 -0.871 -0.001 -0.001 0 -0.031 -0.026 -0.016 -0.02

systmHPmp:2 0.039 0 -0.001 -0.834 -0.002 0 -0.043 -0.036 -0.022 -0.028 0.001

systmS-EB:2 0.041 0 -0.001 -0.002 -0.844 0 -0.046 -0.039 -0.023 -0.03 0.001 0.002

systmS-GB:2 0.007 0 0 0 0 -0.935 -0.008 -0.007 -0.004 -0.005 0 0

systmGs:3-5 0.029 0 -0.911 -0.001 -0.001 0 -0.026 -0.032 -0.016 -0.021 0.86 0.001

systmHP:3-5 0.04 0 -0.002 -0.859 -0.002 0 -0.036 -0.043 -0.022 -0.029 0.001 0.827

sysS-EB:3-5 0.042 0 -0.001 -0.002 -0.863 0 -0.038 -0.045 -0.024 -0.03 0.001 0.002

sysS-GB:3-5 0.007 0 0 0 0 -0.965 -0.007 -0.008 -0.004 -0.005 0 0

systmGs:c6+ 0.018 0 -0.568 -0.001 -0.001 0 -0.017 -0.017 -0.033 -0.013 0.536 0.001

systmHPm:6+ 0.023 0 -0.001 -0.483 -0.001 0 -0.02 -0.021 -0.041 -0.016 0.001 0.465

systS-EB:6+ 0.025 0 -0.001 -0.001 -0.514 0 -0.023 -0.024 -0.046 -0.018 0.001 0.001

systS-GB:6+ 0.006 0 0 0 0 -0.78 -0.005 -0.006 -0.011 -0.004 0 0

58

systmGs:ccU 0.023 0 -0.743 0 0 0 -0.021 -0.022 -0.013 -0.032 0.698 0.001

systmHPmp:U 0.029 0 -0.001 -0.621 -0.001 0 -0.026 -0.027 -0.016 -0.041 0.001 0.596

systmS-EB:U 0.032 0.001 0 0 -0.664 0 -0.029 -0.03 -0.018 -0.044 0.001 0.001

systmS-GB:U 0.006 0 0 0 0 -0.834 -0.006 -0.006 -0.004 -0.009 0 0

sysELEC:... 0.045 0.038 -0.119 -0.165 -0.185 -0.03 0 0 0 0.002 0.001 0.002

systmGs:... 0.001 0.019 -0.256 -0.005 -0.006 -0.001 0 0 0 0 0.001 0

systmHP:... 0 0.023 -0.002 -0.328 -0.003 -0.001 0 0 0 0 0 -0.003

sysS-EB:... 0 0.026 -0.004 -0.006 -0.314 -0.001 0 0 0 0 0 0

sysS-GB:... 0 0.007 -0.001 -0.001 -0.002 -0.184 0 0 0 0 0 0

sS-EB:2

sS-GB:2

sG:3-5

sHP:3-

sS-EB:3

sS-GB:3

syG:6+

sHP:6+

sS-EB:6

sS-GB:6

sysG:U

syHP:U

nct

systemGas

systemHtPmp

systmSlr-EB

systmSlr-GB

occ2

occ3-5

occ6+

occUnknown

systmGs:cc2

systmHPmp:2

systmS-EB:2

systmS-GB:2 0

systmGs:3-5 0.001 0

systmHP:3-5 0.002 0 0.001

sysS-EB:3-5 0.835 0 0.001 0.002

sysS-GB:3-5 0 0.934 0 0 0

systmGs:c6+ 0.001 0 0.561 0.001 0.001 0

systmHPm:6+ 0.001 0 0.001 0.479 0.001 0 0.001

systS-EB:6+ 0.498 0 0.001 0.001 0.509 0 0.002 0.002

systS-GB:6+ 0 0.754 0 0 0 0.778 0 0 0.001

systmGs:ccU 0.001 0 0.73 0.001 0.001 0 0.455 0.001 0.001 0 systmHPmp:

U 0.001 0 0.001 0.614 0.001 0 0.001 0.346 0.001 0 0.001

systmS-EB:U 0.639 0 0.001 0.001 0.653 0 0.001 0.001 0.389 0 0.001 0.002

systmS-GB:U 0 0.805 0 0 0 0.831 0 0 0 0.671 0 0

sysELEC:... 0 0 0.001 0.003 0 0 0 0.001 0 0 -0.006 -0.001

systmGs:... 0 0 0.001 0 0 0 -0.001 0 0 0 0.021 0

systmHP:... 0 0 0 -0.003 0 0 0 -0.002 0 0 0 0.006

sysS-EB:... 0.001 0 0 0 0.002 0 0 0 0.001 0 0 0

sysS-GB:... 0 0.002 0 0 0 0.002 0 0 0 0.005 0 0

sS-EB:U sS-GB:U sELEC: sG:... sHP:.. sS-EB:.

nct

59

systemGas

systemHtPmp

systmSlr-EB

systmSlr-GB

occ2

occ3-5

occ6+

occUnknown

systmGs:cc2

systmHPmp:2

systmS-EB:2

systmS-GB:2

systmGs:3-5

systmHP:3-5

sysS-EB:3-5

sysS-GB:3-5

systmGs:c6+

systmHPm:6+

systS-EB:6+

systS-GB:6+

systmGs:ccU

systmHPmp:U

systmS-EB:U

systmS-GB:U 0

sysELEC:... -0.006 -0.001

systmGs:... 0 0 0.033

systmHP:... 0 0 0.018 0.001

sysS-EB:... 0.018 0 0.036 0.002 0.001

sysS-GB:... 0 0.015 0.009 0 0 0


Fixed effects

Estimate MCMC mean

HPD95 lower HPD95 upper pMCMC Pr(>|t|)

(Intercept) 15.6916 15.6808 15.4692 15.8765 0.0001 0

nct 1.2764 1.2769 1.2652 1.2884 0.0001 0

systemGas -6.2626 -6.2766 -6.4228 -6.1289 0.0001 0

systemHeat Pump -3.5501 -3.5374 -3.6396 -3.4366 0.0001 0

systemSolar - Electric Boosted -4.2016 -4.1963 -4.2943 -4.102 0.0001 0

systemSolar - Gas Boosted -6.4969 -6.4196 -7.0315 -5.8531 0.0001 0

occ2 6.5817 6.5901 6.3612 6.8111 0.0001 0

occ3-5 13.9616 13.9748 13.7562 14.189 0.0001 0

occ6+ 20.0526 20.0717 19.7051 20.4277 0.0001 0

occUnknown 10.304 10.3148 10.0375 10.6036 0.0001 0

60

systemGas:occ2 -0.6748 -0.706 -0.8642 -0.5512 0.0001 0

systemHeat Pump:occ2 -0.5823 -0.5926 -0.6991 -0.487 0.0001 0

systemSolar - Electric Boosted:occ2 -0.8561 -0.842 -0.9409 -0.7418 0.0001 0

systemSolar - Gas Boosted:occ2 -0.6618 -0.7332 -1.3489 -0.1299 0.0188 0.0299

systemGas:occ3-5 -1.8688 -1.9081 -2.0559 -1.7563 0.0001 0

systemHeat Pump:occ3-5 -0.9977 -0.9902 -1.094 -0.8874 0.0001 0

systemSolar - Electric Boosted:occ3-5 -0.4283 -0.419 -0.5131 -0.3186 0.0001 0

systemSolar - Gas Boosted:occ3-5 -0.8113 -0.919 -1.5122 -0.3308 0.0026 0.006

systemGas:occ6+ -3.6194 -3.6976 -3.9297 -3.4572 0.0001 0

systemHeat Pump:occ6+ -0.936 -0.9612 -1.1421 -0.7792 0.0001 0

systemSolar - Electric Boosted:occ6+ -0.0591 -0.0239 -0.1865 0.1321 0.7792 0.4713

systemSolar - Gas Boosted:occ6+ -2.8916 -2.8986 -3.6346 -2.1827 0.0001 0

systemGas:occUnknown -1.4511 -1.4638 -1.6567 -1.2856 0.0001 0

systemHeat Pump:occUnknown -0.7407 -0.7498 -0.8893 -0.6062 0.0001 0

systemSolar - Electric Boosted:occUnknown 0.4458 0.4545 0.3286 0.5817 0.0001 0

systemSolar - Gas Boosted:occUnknown -0.2578 -0.3575 -1.0146 0.3566 0.3066 0.4516

systemELEC:months.after.int.date 0.0093 0.0091 0.0084 0.0099 0.0001 0

systemGas:months.after.int.date 0.0048 0.0048 0.0026 0.0067 0.0001 0

systemHeat Pump:months.after.int.date 0.0206 0.0208 0.0191 0.0225 0.0001 0

systemSolar - Electric Boosted:months.after.int.date 0.0183 0.0183 0.0167 0.0198 0.0001 0

systemSolar - Gas Boosted:months.after.int.date 0.0343 0.0338 0.028 0.0394 0.0001 0

Random effects

Groups Name Std.Dev. MCMC median MCMC mean HPD95 lower HPD95 upper

1 id (Intercept) 10.1648 5.3377 5.3531 5.316 5.3564

2 id nct 1.1465 1.1236 1.1235 1.1149 1.1323

3 Residual

6.1988 6.3136 6.3131 6.308 6.319

OFF PEAK SYSTEM REPLACEMENT SUBSET – NET SAVINGS (MODEL SPECIFICATION 2)


Formula: tot ~ (1 | id) + (0 + nct | id) + system + nct

Data: hws_cl


11125998 11126109 -

5562990 11125946 11125980

Random effects:


61

id (Intercept) 121.1728 11.0079

id nct 1.2643 1.1244

Residual

35.2221 5.9348


Fixed effects:

Estimate

Std. Error t value Pr(>|t|)

(Intercept) 27.03369 0.073464 368 <2e-16 ***

systemGas -8.82303 0.026298 -335.5 <2e-16 ***

systemHeat Pump -4.37219 0.016916 -258.5 <2e-16 ***

systemSolar - Electric Boosted -4.68156 0.01443 -324.4 <2e-16 ***

systemSolar - Gas Boosted -8.46129 0.065649 -128.9 <2e-16 ***

nct 1.261335 0.007744 162.9 <2e-16 ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


(Intr) systmG systHP syS-EB syS-GB

systemGas -0.024 systemHtPmp -0.037 0.002

systmSlr-EB -0.042 0.003 0.004 systmSlr-GB -0.009 0.001 0.001 0.001

nct -0.005 0.032 0.049 0.058 0.013


Fixed effects

Estimate MCMCmean HPD95lower HPD95upper pMCMC Pr(>|t|)

(Intercept) 27.034 27.036 26.965 27.104 0.0001 0

systemGas -8.823 -8.819 -8.872 -8.766 0.0001 0

systemHeat Pump -4.372 -4.378 -4.411 -4.345 0.0001 0



nct 1.261 1.262 1.246 1.276 0.0001 0

Random effects

Groups Name Std.Dev.

MCMC median MCMC mean

HPD95 lower

HPD95 upper

1 id (Intercept) 11.0079 5.2989 5.3193 5.2758 5.3252

2 id nct 1.1244 1.1015 1.1015 1.0908 1.1117

62

3 Residual

5.9348 6.0722 6.0715 6.0652 6.0794

OFF PEAK SYSTEM REPLACEMENT SUBSET – CONTROLLED LOAD SAVINGS (MODEL

SPECIFICATION 2)


Formula: cl ~ (1 | id) + (0 + nct_cl | id) + system + nct_cl

Data: hws_cl


7843266 7843377 -

3921624 7843203 7843248

Random effects:


id (Intercept) 7.6322 2.76264

id nct_cl 0.6305 0.79404

Residual

5.2616 2.29382


Fixed effects:

Estimate

Std. Error t value Pr(>|t|)

(Intercept) 8.516074 0.018529 459.6 <2e-16 ***

systemGas -9.1022 0.009814 -927.5 <2e-16 ***

systemHeat Pump -4.31889 0.006339 -681.3 <2e-16 ***

systemSolar - Electric Boosted -4.52555 0.005394 -839 <2e-16 ***

systemSolar - Gas Boosted -8.98204 0.024526 -366.2 <2e-16 ***

nct_cl 1.001313 0.005522 181.3 <2e-16 ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


(Intr) systmG systHP syS-EB syS-GB

systemGas -0.036 systemHtPmp -0.055 0.003

systmSlr-EB -0.062 0.003 0.005 systmSlr-GB -0.014 0.001 0.001 0.001

nct_cl -0.005 0.022 0.039 0.042 0.009

63


Fixed effects

Estimate

MCMC mean

HPD95 lower

HPD95 upper pMCMC Pr(>|t|)

(Intercept) 8.516 8.516 8.4914 8.539 0.0001 0

systemGas -9.102 -9.08 -9.0995 -9.061 0.0001 0

systemHeat Pump -4.319 -4.319 -4.3314 -4.306 0.0001 0



nct_cl 1.001 1.001 0.9914 1.012 0.0001 0

Random effects

Groups Name Std.Dev.

MCMC median

MCMC mean

HPD95 lower

HPD95 upper

1 id (Intercept) 2.7626 1.7655 1.7679 1.7539 1.7761

2 id nct_cl 0.794 0.7429 0.743 0.7356 0.7499

3 Residual

2.2938 2.3183 2.3182 2.3157 2.3208

64

APPENDIX E: MPMC methodology and detailed results

The MPMC procedure is conceptually simple but computationally intensive, and is specifically

designed for estimating energy and/or water savings using only billing data and an intervention date

for each participant household5. The core logic is:

Each participant household is ‘paired’ with a non-participant household that has a similar

consumption pattern prior to the start of the HPSP.

Since each paired non-participant household had a similar consumption pattern to its paired

participant household, it serves to indicate what the participant household’s consumption

would have been had it not participated in the program.

Since we have an estimate of what each participant household would probably have

consumed, and we observe what each participant household did consume, taking the

difference of these gives us an estimate of the savings attributable to participation.

For more detail on the method, see Fyfe et al. (2010).

The MPMC approach implicitly controls for factors such as weather, changes in appliance ownership

and other trends.

HOUSEHOLDS THAT SWITCHED TO GAS HOT WATER

The average saving for households that switched to gas storage or instantaneous hot water systems

is 7.13 ± 0.19 kWh/day. This is the figure for all tariffs combined. The percentage saving for

participants are based on the average post intervention consumption of the corresponding control

group cohort.

The average saving estimate, the difference between the blue (non-participant) and red

(participants) cohorts is shown in Figure E-1. A five month period of two months either side of the

intervention month plus the intervention month is excluded from the analysis due to billing data

potentially covering both the pre and post intervention period. The saving by calendar month

demonstrating the seasonality of the saving is shown in Figure E-2.

5 Where detailed demographic and other data is available about both the participant and non-participant

households (such as dwelling type, household income and structure, appliance ownership, and so on), a regression model may be more appropriate, and provide more detailed results.

65

Figure E-1 Pre and post intervention consumption of gas HWS participant and matched pair control cohorts

Figure E-2 Gas HWS savings time series

HOUSEHOLDS THAT SWITCHED TO GAS-BOOSTED SOLAR HOT WATER

The average saving for households that switched to a solar gas boosted system is 5.61 ± 0.55

kWh/day.

66

Figure E-3 Pre and post intervention consumption of gas-boosted solar HWS participant and matched pair

control cohorts

Figure E-4 Gas-boosted solar HWS savings time series

HOUSEHOLDS THAT SWITCHED TO ELECTRIC-BOOSTED SOLAR

The average overall saving for households that switched to a electric-boosted solar system is 3.64 ±

0.12 kWh/day.

67

Figure E-5 Pre and post intervention consumption of electric-boosted solar HWS participant and matched

pair control cohorts

Figure E-6 Electric-boosted solar HWS savings time series

HOUSEHOLDS THAT INSTALLED A HEAT PUMP

The average saving for households that switched to a heat pump system is 3.14 ± 0.14.

68

Figure E-7 Pre and post intervention consumption of heat pump HWS participant and matched pair control

cohorts

Figure E-8 Heat pump HWS savings time series

quantitative analysis of electricity savings from the home ... · savings in gas or other fuel...

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