Artificial neural networks for short term electrical load forecasting
- Stone and Webster Advanced Systems Development Services, Inc., Boston, MA (United States)
The accurate prediction of hourly electrical demand one or more days ahead is of great economic importance to electric utilities for generation unit dispatch and unit commitment. Artificial neural networks for pattern recognition are developed to identify days in the historical record that are most similar to the days being forecasted, to use for load prediction. Artificial neural networks are also used to generate linear and nonlinear multivariate time series models, to project demands forward in time. The genetic algorithm is used to select the optimal set of independent variables for forecasting. Techniques are developed to combine forecasts derived from independent methods, to achieve better accuracy than any single forecast. In this way, artificial neural networks can be used to generate practical, accurate short-term electrical load forecasts.
- OSTI ID:
- 103719
- Report Number(s):
- CONF-950414--
- Country of Publication:
- United States
- Language:
- English
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