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Short term load forecasting using fuzzy neural networks

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4 Author(s)
Bakirtzis, A.G. ; Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki, Greece ; Theocharis, J.B. ; Kiartzis, S.J. ; Satsios, K.J.

This paper presents the development of a fuzzy system for short term load forecasting. The fuzzy system has the network structure and the training procedure of a neural network and is called a fuzzy neural network (FNN). An FNN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that the output of the FNN adequately matches the available historical load data. Once trained, the FNN can be used to forecast future loads. Test results show that the FNN can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks

Published in:

Power Systems, IEEE Transactions on  (Volume:10 ,  Issue: 3 )

Date of Publication:

Aug 1995

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