Title :
Neuro-fuzzy approaches to short-term electrical load forecasting
Author :
Bartkiewicz, Witold
Author_Institution :
Dept. of Comput. Sci., Lodz Univ., Poland
Abstract :
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 the combination of the cluster estimation and ordinary least squares method are applied. For nonlinear antecedent modelling purposes the fuzzy switched ensemble of feedforward neural networks was used. The performance of the models is compared for two-day ahead peak load prediction in the distribution network
Keywords :
fuzzy neural nets; learning (artificial intelligence); least squares approximations; load forecasting; power distribution planning; power engineering computing; Takagi-Sugeno models; cluster estimation; distribution network; electrical load forecasting; feedforward neural networks; fuzzy neural networks; identification; learning algorithms; least squares method; peak load prediction; Economic forecasting; Fuzzy sets; Fuzzy systems; Least squares methods; Load forecasting; Neural networks; Power system planning; Predictive models; Takagi-Sugeno model; Weather forecasting;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859401
