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Artificial Intelligence in Electricity Market Operations and Management
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Author(s): Zhao Y. Dong (The University of Queensland, Australia), Tapan K. Saha (The University of Queensland, Australia)and Kit P. Wong (The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2006
Pages: 24
Source title:
Business Applications and Computational Intelligence
Source Author(s)/Editor(s): Kevin Voges (University of Canterbury, New Zealand)and Nigel Pope (Griffith University, Australia)
DOI: 10.4018/978-1-59140-702-7.ch008
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Abstract
This chapter introduces advanced techniques such as artificial neural networks, wavelet decomposition, support vector machines, and data-mining techniques in electricity market demand and price forecasts. It argues that various techniques can offer different advantages in providing satisfactory demand and price signal forecast results for a deregulated electricity market, depending on the specific needs in forecasting. Furthermore, the authors hope that an understanding of these techniques and their application will help the reader to form a comprehensive view of electricity market data analysis needs, not only for the traditional time-series based forecast, but also the new correlation-based, price spike analysis.
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