%0 Journal Article
%T Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference
%A Evans Nyasha Chogumaira
%A Takashi Hiyama
%J Energy and Power Engineering
%P 9-16
%@ 1947-3818
%D 2011
%I Scientific Research Publishing
%R 10.4236/epe.2011.31002
%X 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.
%K Electricity Price Forecasting
%K Short-Term Load Forecasting
%K Electricity Markets
%K Artificial Neural Networks
%K Fuzzy Logic
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=4003