Residential end-use load-shape estimation. Volume 1. Methodology and results of statistical disaggregation from whole-house metered loads. Final report
Abstract
End-use load shapes are important for the development of improved load forecasting techniques, analysis of load management and costing/rate making. A methodology for estimating end-use load shapes using hourly whole-house metered load data, household demographic survey data, and weather data (temperature) is presented. Although the focus of the project was on the residential sector, the techniques developed can also be applied to the industrial and commercial sectors. In the present approach, the coupling of lifestyle and weather in load demand is clearly modeled. Weather-independent load is modeled with Fourier-like terms (sine and cosine functions) and dummy variables, and weather-dependent load is represented by a thermodynamics-based nonlinear dynamic model exhibiting heat build-up effect, thermostat set-point variation, and appliance saturation. Automatic data pre-processing is incorporated to eliminate anomalous data using pattern recognition techniques. The overall methodology provides an effective means for end-use load shape modeling. In particular, the effect of weather on electricity demand, which concerns power system forecasters and planners, is accurately represented.
- Authors:
- Publication Date:
- Research Org.:
- Scientific Systems, Inc., Cambridge, MA (USA)
- OSTI Identifier:
- 5746649
- Report Number(s):
- EPRI-EM-4525-Vol.1
ON: TI86920280
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY AND ECONOMY; ELECTRIC POWER; LOAD MANAGEMENT; ENERGY DEMAND; FORECASTING; RESIDENTIAL SECTOR; DEMAND; MANAGEMENT; POWER; 292000* - Energy Planning & Policy- Supply, Demand & Forecasting; 298000 - Energy Planning & Policy- Consumption & Utilization
Citation Formats
Usoro, P B, and Schick, I C. Residential end-use load-shape estimation. Volume 1. Methodology and results of statistical disaggregation from whole-house metered loads. Final report. United States: N. p., 1986.
Web.
Usoro, P B, & Schick, I C. Residential end-use load-shape estimation. Volume 1. Methodology and results of statistical disaggregation from whole-house metered loads. Final report. United States.
Usoro, P B, and Schick, I C. 1986.
"Residential end-use load-shape estimation. Volume 1. Methodology and results of statistical disaggregation from whole-house metered loads. Final report". United States.
@article{osti_5746649,
title = {Residential end-use load-shape estimation. Volume 1. Methodology and results of statistical disaggregation from whole-house metered loads. Final report},
author = {Usoro, P B and Schick, I C},
abstractNote = {End-use load shapes are important for the development of improved load forecasting techniques, analysis of load management and costing/rate making. A methodology for estimating end-use load shapes using hourly whole-house metered load data, household demographic survey data, and weather data (temperature) is presented. Although the focus of the project was on the residential sector, the techniques developed can also be applied to the industrial and commercial sectors. In the present approach, the coupling of lifestyle and weather in load demand is clearly modeled. Weather-independent load is modeled with Fourier-like terms (sine and cosine functions) and dummy variables, and weather-dependent load is represented by a thermodynamics-based nonlinear dynamic model exhibiting heat build-up effect, thermostat set-point variation, and appliance saturation. Automatic data pre-processing is incorporated to eliminate anomalous data using pattern recognition techniques. The overall methodology provides an effective means for end-use load shape modeling. In particular, the effect of weather on electricity demand, which concerns power system forecasters and planners, is accurately represented.},
doi = {},
url = {https://www.osti.gov/biblio/5746649},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu May 01 00:00:00 EDT 1986},
month = {Thu May 01 00:00:00 EDT 1986}
}