Multilayer neuro-fuzzy network for short term electric load forecasting

Authors:
Yevgeniy Bodyanskiy;Sergiy Popov;Taras Rybalchenko
Affiliations:
Control Systems Research Laboratory, Kharkiv National University of Radio Electronics;Control Systems Research Laboratory, Kharkiv National University of Radio Electronics;Control Systems Research Laboratory, Kharkiv National University of Radio Electronics
Venue:
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
Year:
2008

Citing 2
Cited 1

Neural Networks for Optimization and Signal Processing

Neural Networks for Optimization and Signal Processing
Computational Intelligence Techniques for Short-Term Electric Load Forecasting

Journal of Intelligent and Robotic Systems

Electric load forecasting based on locally weighted support vector regression

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

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Abstract

The problem of short term electric load forecasting is considered in the case when a part of input variables is given in a nonnumeric form. Novel neuro-fuzzy network architecture and learning algorithms are proposed, which enable high-rate processing of information given in different measurements scales (quantitative, ordinal, and nominal). Types and parameters of the employed membership functions may be determined by the amount of available explicit prior knowledge. Experimental comparison to a traditional neural network confirms superiority of the proposed approach.