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Title: An approach to distribution short-term load forecasting

Abstract

This paper reports on the developments and findings of the Distribution Short-Term Load Forecaster (DSTLF) research activity. The objective of this research is to develop a distribution short-term load forecasting technology consisting of a forecasting method, development methodology, theories necessary to support required technical components, and the hardware and software tools required to perform the forecast The DSTLF consists of four major components: monitored endpoint load forecaster (MELF), nonmonitored endpoint load forecaster (NELF), topological integration forecaster (TIF), and a dynamic tuner. These components interact to provide short-term forecasts at various points in the, distribution system, eg., feeder, line section, and endpoint. This paper discusses the DSTLF methodology and MELF component MELF, based on artificial neural network technology, predicts distribution endpoint loads for an hour, a day, and a week in advance. Predictions are developed using time, calendar, historical load, and weather data. The overall DSTLF architecture and a prototype MELF module for retail endpoints have been developed. Future work will be focused on refining and extending MELF and developing NELF and TIF capabilities.

Authors:
;
Publication Date:
Research Org.:
Pacific Northwest Lab., Richland, WA (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
67750
Report Number(s):
PNL-SA-26114; CONF-9503142-2
ON: DE95011418; TRN: 95:004570
DOE Contract Number:  
AC06-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Workshop on environmental and energy applications of neural networks conference, Richland, WA (United States), 30-31 Mar 1995; Other Information: PBD: Mar 1995
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING AND POLICY; 20 FOSSIL-FUELED POWER PLANTS; ELECTRIC UTILITIES; LOAD MANAGEMENT; LOAD ANALYSIS; FORECASTING; POWER DEMAND; POWER DISTRIBUTION; NEURAL NETWORKS; AUTOMATION

Citation Formats

Stratton, R.C., and Gaustad, K.L. An approach to distribution short-term load forecasting. United States: N. p., 1995. Web.
Stratton, R.C., & Gaustad, K.L. An approach to distribution short-term load forecasting. United States.
Stratton, R.C., and Gaustad, K.L. Wed . "An approach to distribution short-term load forecasting". United States. https://www.osti.gov/servlets/purl/67750.
@article{osti_67750,
title = {An approach to distribution short-term load forecasting},
author = {Stratton, R.C. and Gaustad, K.L.},
abstractNote = {This paper reports on the developments and findings of the Distribution Short-Term Load Forecaster (DSTLF) research activity. The objective of this research is to develop a distribution short-term load forecasting technology consisting of a forecasting method, development methodology, theories necessary to support required technical components, and the hardware and software tools required to perform the forecast The DSTLF consists of four major components: monitored endpoint load forecaster (MELF), nonmonitored endpoint load forecaster (NELF), topological integration forecaster (TIF), and a dynamic tuner. These components interact to provide short-term forecasts at various points in the, distribution system, eg., feeder, line section, and endpoint. This paper discusses the DSTLF methodology and MELF component MELF, based on artificial neural network technology, predicts distribution endpoint loads for an hour, a day, and a week in advance. Predictions are developed using time, calendar, historical load, and weather data. The overall DSTLF architecture and a prototype MELF module for retail endpoints have been developed. Future work will be focused on refining and extending MELF and developing NELF and TIF capabilities.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 1995},
month = {Wed Mar 01 00:00:00 EST 1995}
}

Conference:
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