Neuro-Fuzzy Approaches to Short-Term Electrical Load Forecasting

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Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Year:
2000

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Cited 1

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CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence

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Abstract

In this paper, we investigate the application of the Takagi-Sugeno fuzzy models to short-term electrical load forecasting problem. Several learning algorithms for these type fuzzy systems are discussed. For identification of the models with linear antecedents has been applied the combination of the cluster estimation and ordinary least squares method. For nonlinear antecedents modeling purposes has been used the fuzzy switched ensemble of feedforward neural networks. The performance of the models is compared for two-day ahead peak load prediction in the distribution network.