Neuro-fuzzy approaches to short-term electrical load forecasting

Author

Bartkiewicz, Witold

Author_Institution

Dept. of Comput. Sci., Lodz Univ., Poland

Volume

6

fYear

2000

fDate

2000

Firstpage

229

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.859401

Filename

859401