Home Performance with ENERGY STAR: Utility Bill Analysis on Homes Participating in Austin Energy's Program

Belzer, David B.; Mosey, Grant; Plympton, P.; Dagher, Leila

doi:10.2172/910503

Cited by 22 publications

(9 citation statements)

References 3 publications

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“…Methods for disaggregating billing data have been previously described (ASHRAE 2002, Belzer 2007, Sondregger 1998. ASHRAE Guideline 14-2002 was reviewed for applicability to the task of disaggregating the large sample of PG&E billing data.…”

Section: Review Of Disaggregation Methods In the Literaturementioning

confidence: 99%

BEopt-CA (Ex): A Tool for Optimal Integration of EE, DR and PV in Existing California Homes

Christensen¹,

Horowitz²,

Maguire³

et al. 2014

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This project targeted the development of a software tool, BEopt-CA (Ex) (Building Energy Optimization Tool for California Existing Homes), that aims to facilitate balanced integration of energy efficiency (EE), demand response (DR), and photovoltaics (PV) in the residential retrofit 1 market. The intent is to provide utility program managers and contractors in the EE/DR/PV marketplace with a means of balancing the integration of EE, DR, and PV.The National Renewable Energy Laboratory's BEopt software was enhanced by adding capabilities in the following areas:• Existing home retrofit analysis • Retrofit measures and cost data • Utility tariff capabilities • Utility cost-effectiveness tests • Incentives for PV and whole-house efficiency • Demand response.These new BEopt-CA (Ex) capabilities are now available in the current version of BEopt (https://beopt.nrel.gov) and can be accessed by selecting the California-specific mode upon launching the program.BEopt was connected to the new California Simulation Engine (CSE) to provide capability for software-to-software comparisons with EnergyPlus via the BEopt Test Suite. 2 Further integration of BEopt and CSE could provide: (1) parametric (and optimization) capabilities for CSE in Title 24 development; and (2) a front end for a public CSE-based Title 24 user tool. This report includes example analysis results to demonstrate some of the new BEopt capabilities. Utility cost test results are given for optimized building designs combining EE and PV on the path to zero net energy over a range of rate types, house types, fuel types, energy consumption levels, and climates.1 Retrofit analysis was a major focus of the project, but many of the new BEopt capabilities are also applicable to new construction. 2 The BEopt Test Suite automates thousands of runs for multiple simulation engines across a full range of building characteristics and energy efficiency measures. Statistically relevant end-use data from two existing California communities were also developed for use in validation.6 Conclusions .

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Section: Review Of Disaggregation Methods In the Literaturementioning

confidence: 99%

BEopt-CA (Ex): A Tool for Optimal Integration of EE, DR and PV in Existing California Homes

Christensen¹,

Horowitz²,

Maguire³

et al. 2014

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show abstract

“…In view of the long-run relationships between the household electricity consumption and other variables in the models, the long-run and short-run elasticities are further examined. The corresponding ARDL specifications of the long-run models are (5) and (6) for Singapore and Shanghai, respectively. As discussed in section 2.2.2, a maximum of 4 lags was employed in Eqs.…”

Section: Ardl Cointegration Analysismentioning

confidence: 99%

“…As discussed in section 2.2.2, a maximum of 4 lags was employed in Eqs. (5) and (6) and the optimal lag length is determined by minimizing the AIC. The specifications with the optimal lag length are ARDL(1,1,3,2) and ARDL(3,1,3,4) for Singapore and Shanghai, respectively.…”

Section: Ardl Cointegration Analysismentioning

confidence: 99%

“…Policy makers and investors often rely on these relationships to evaluate the impact of energy conservation and governmental directives on electricity use [1,2]. Understanding the fluctuations of household electricity consumption also serves an important input for the designing and optimization of new building-related energy plans, such as net zero energy building (NZEB) technologies [3,4] and home performance with energy star programs [5,6]. A great number of studies [2,[7][8][9][10][11][12] have been conducted to study the effects of weather (e.g., temperature and rainy days), and economic factors (e.g., income, electricity tariff, GDP, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Variation of household electricity consumption and potential impact of outdoor PM2.5 concentration: A comparison between Singapore and Shanghai

et al. 2017

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The auto-regressive distributed lag (ARDL) bound testing approach was used to study the relationships between the monthly household electricity consumption and outdoor PM 2.5 concentration with the consideration of ambient temperature and the number of rainy days for Singapore and Shanghai. It is shown that there are significant long-run relationships between the household electricity consumption and the regressors for both Singapore and Shanghai. For Singapore, a 20% increase in the PM 2.5 concentration of a single month is in the long-run significantly related to a 0.8% increase in the household electricity consumption. This corresponds to an electricity overconsumption of 5.0 GWh, a total of 0.7-1.0 million USD in electricity cost, and 2.1 kilotons of CO 2 emission associated with electricity generation. For Shanghai, a 20% decrease in the PM 2.5 concentration of a single month is in the long-run significantly related to a 2.2% decrease in the household electricity consumption. This corresponds to a 35.0 GWh decrease in the overall household electricity consumption, 1.6-5.1 million USD decrease in electricity cost, and 17.5 kilotons of CO 2 emission. The results suggest that the cost of electricity consumption should be included in the economic cost analysis of PM 2.5 pollution in the future. A 1 o C increase in the monthly temperature is in the long-run significantly related to a 13.6% increase in the monthly electricity consumption for Singapore, while a 30 degree days increase in heating & cooling days () is in the long-run significantly related to a 24.9% increase in the monthly electricity consumption for Shanghai. A 5-day increase in the number of rainy days per month is in the long-run significantly related to a 3.0% and 5.8% increase in the monthly electricity consumption for Singapore and Shanghai, respectively.

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“…A 2007 National Renewable Energy Laboratory and Pacific Northwest National Laboratory study of Austin Electric's HPwES program found that in more than 6,000 homes in Austin, Texas, the measured savings were in the range of 25%-35%, based on utility bill data (Belzer, 2007). These savings are in the range of BA goals.…”

Section: Introductionmentioning

confidence: 99%

Reducing Energy Use in Existing Homes by 30%: Learning From Home Performance with ENERGY STAR

Liaukus

2014

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Home Performance with ENERGY STAR: Utility Bill Analysis on Homes Participating in Austin Energy's Program

Cited by 22 publications

References 3 publications

BEopt-CA (Ex): A Tool for Optimal Integration of EE, DR and PV in Existing California Homes

BEopt-CA (Ex): A Tool for Optimal Integration of EE, DR and PV in Existing California Homes

Variation of household electricity consumption and potential impact of outdoor PM2.5 concentration: A comparison between Singapore and Shanghai

Reducing Energy Use in Existing Homes by 30%: Learning From Home Performance with ENERGY STAR

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