Short term load forecasting using fuzzy neural networks

Journal Article · Tue Aug 01 00:00:00 EDT 1995 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/59.466494· OSTI ID:163051

Bakirtzis, A G; Theocharis, J B; Kiartzis, S J; Satsios, K J ^[1]

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1. Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering

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 Fuzzy Neural Network (FNN). A 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.

• OSTI ID: 163051
• Report Number(s): CONF-950103--
• Journal Information: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 3 Vol. 10; ISSN ITPSEG; ISSN 0885-8950
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
COMPUTER CALCULATIONS
FORECASTING
FUZZY LOGIC
NEURAL NETWORKS
POWER DEMAND
TRAINING