Short-term electric load forecasting using neural networks
Conference
·
OSTI ID:212509
Short-term electric load forecasting (STELF) plays an important role in electric utilities, and several techniques are used to perform these predictions and system modelings. Recently, artificial neural networks (ANN`s) have been implemented for STELF with some success. This paper will examine improved STELF by optimization of ANN techniques. The strategy for the research involves careful selection of input variables and utilization of effective generalization. Some results have been obtained which show that, with the selection of another input variable, the ANN`s use for STELF can be improved.
- Research Organization:
- Iowa State Univ. of Science and Technology, Ames, IA (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG02-92ER75700
- OSTI ID:
- 212509
- Report Number(s):
- CONF-9305433-1; ON: DE96007023; TRN: AHC29608%%54
- Resource Relation:
- Conference: Iowa State Univeristy electric power research center power affiliates research program, Iowa City, IA (United States), May 1993; Other Information: PBD: [1993]
- Country of Publication:
- United States
- Language:
- English
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