Conceptual approach to the application of neural network for short-term load forecasting

T. M. Peng, N. F. Hubele, G. G. Karady

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

The feasibility of using a simple neural network for short-term load forecasting is investigated. A combined linear and nonlinear neural network is developed. The forecasts are computed using weights which are reestimated using only very recent observations. The model operation is tested by using load data obtained from a winter-peaking utility in the Northeastern USA. The results show that the error in most weeks is small, less than 4-5%. This validation test proves that the method is feasible and able to produce accurate forecasts under normal conditions.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
PublisherPubl by IEEE
Pages2942-2945
Number of pages4
Volume4
StatePublished - 1990
Event1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA
Duration: May 1 1990May 3 1990

Other

Other1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4)
CityNew Orleans, LA, USA
Period5/1/905/3/90

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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    Peng, T. M., Hubele, N. F., & Karady, G. G. (1990). Conceptual approach to the application of neural network for short-term load forecasting. In Proceedings - IEEE International Symposium on Circuits and Systems (Vol. 4, pp. 2942-2945). Publ by IEEE.