Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

Evans Nyasha Chogumaira, Takashi Hiyama

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DOI: 10.4236/epe.2011.31002                

Abstract

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Keywords

Electricity Price Forecasting, Short-Term Load Forecasting, Electricity Markets, Artificial Neural Networks, Fuzzy Logic

Conflicts of Interest

The authors declare no conflicts of interest.

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