Artificial Intelligence in Electricity Market Operations and Management
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
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.