An approach to distribution short-term load forecasting

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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 ... continued below

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6 p.

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Stratton, R. C. & Gaustad, K. L. March 1995.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Pacific Northwest Laboratory
    Publisher Info: Pacific Northwest Lab., Richland, WA (United States)
    Place of Publication: Richland, Washington

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Description

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.

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6 p.

Notes

OSTI as DE95011418

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  • Workshop on environmental and energy applications of neural networks conference, Richland, WA (United States), 30-31 Mar 1995

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  • Other: DE95011418
  • Report No.: PNL-SA--26114
  • Report No.: CONF-9503142--2
  • Grant Number: AC06-76RL01830
  • Office of Scientific & Technical Information Report Number: 67750
  • Archival Resource Key: ark:/67531/metadc710503

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • March 1995

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  • Sept. 12, 2015, 6:31 a.m.

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  • April 7, 2016, 2:22 p.m.

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Stratton, R. C. & Gaustad, K. L. An approach to distribution short-term load forecasting, article, March 1995; Richland, Washington. (digital.library.unt.edu/ark:/67531/metadc710503/: accessed June 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.