Parallel neural network-fuzzy expert system strategy for short-term load forecasting: System implementation and performance evaluation
- National Univ. of Singapore (Singapore). Dept. of Electrical Engineering
- Power Automation Pte Ltd., Singapore (Singapore)
The on-line implementation and results from a hybrid short-term electrical load forecaster that is being evaluated by a power utility are documented in this paper. This forecaster employs a new approach involving a parallel neural-fuzzy expert system, whereby Kohonen`s self organizing feature map with unsupervised learning, is used to classify daily load patterns. Post-processing of the neural network outputs is performed with fuzzy expert system which successfully corrects the load deviations caused by the effects of weather and holiday activity. Being highly automated, little human interference is required during the process of load forecasting. A comparison made between this model and a regression-based model currently being used in the Control Centre has shown a market improvement in load forecasting results.
- OSTI ID:
- 678015
- Journal Information:
- IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 3 Vol. 14; ISSN ITPSEG; ISSN 0885-8950
- Country of Publication:
- United States
- Language:
- English
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