Artificial Intelligence in Electricity Market Operations and Management

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Author(s): Zhao Yang Dong (The University of Queensland, Australia), Tapan Kumar Saha (The University of Queensland, Australia)and Kit Po Wong (The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2008
Pages: 20
Source title: Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Vijayan Sugumaran (Oakland University, Rochester, USA)
DOI: 10.4018/978-1-59904-941-0.ch105

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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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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