Short term daily average and peak load predications using a hybrid intelligent approach

Author

Dash, P.K. ; Satpathy, H.P. ; Rahman, S.

Author_Institution

Regional Eng. Coll., Rourkela, India

Volume

2

fYear

1995

Firstpage

565

Abstract

A fuzzy neural network based on the multilayer perceptron and capable of fuzzy classification of patterns is presented in this paper. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the network. In the supervised learning phase linear Kalman filter equations are used for tuning the weights and membership functions. Extensive tests have been performed on a two-year-utility data for generation of peak and average load profiles for 24- and 168-hours ahead time frames and results for winter and summer months are given to confirm the effectiveness of the new approach

Keywords

Kalman filters; expert systems; fuzzy neural nets; load forecasting; multilayer perceptrons; pattern classification; power system analysis computing; unsupervised learning; fuzzy expert system; fuzzy neural network; fuzzy pattern classification; hybrid intelligent approach; hybrid learning algorithm; linear Kalman filter equations; membership functions; multilayer perceptron; short term daily average load predication; short term daily peak load predications; supervised learning phases; unsupervised learning phases; Artificial neural networks; Backpropagation algorithms; Expert systems; Fuzzy neural networks; Hybrid intelligent systems; Load forecasting; Multilayer perceptrons; Neural networks; Supervised learning; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Energy Management and Power Delivery, 1995. Proceedings of EMPD '95., 1995 International Conference on

Print_ISBN

0-7803-2981-3

Type

conf

DOI

10.1109/EMPD.1995.500789

Filename

500789