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Electricity market clearing price (MCP) forecasting is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. This book presents a mid-term electricity MCP forecasting technique utilizing neural network approach. Forecasting of mid-term MCP is essential for decision making, scheduling and bidding strategy planning purposes. However, forecasting mid-term electricity market clearing price is a very complex non-linear problem. Neural network based approaches can be effective forecasting tools in an environment with high degree of non-linearity and uncertainty. Two new artificial neural networks have been proposed and reported in this book that can be utilized to forecast mid-term daily peak and mid- term hourly electricity market clearing price. Two situations have been considered; market clearing price forecasting under real deregulated electric market and under deregulated electric market with perfect competition.

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Xing Yan received B.A.Sc. in Electrical Engineering at the University of British Columbia in 2006, a Masters in Electrical Engineering at the University of Saskatchewan in 2009 and currently working on his Ph.D. in Electrical Engineering at the University of Saskatchewan. His research interests are deregulated electricity price forecasting.

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