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

Abstract

An example of the second type of inconsistency is shown by the following bills for premise number 2829400. The ending date for the first bill (3/17/2001) is consistent with the starting date for the second bill. However, there is no ending date for either the second or third bills and these bills have duplicate starting dates.An ending date for the second bill of 4/14/2005 can be derived from the number of days in the billing period (28). The missing ending date for the third bill can be filled as 5/20/2005, again consistent with the number of days and the starting date of the fourth bill. The adjusted series of bills, with the changed values in italics, then becomesThe public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY) SUBJECT TERMS

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

“…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.…”

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

“…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.…”

“…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.…”

“…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.)…”

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

“…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.…”