Day-Ahead Electricity Prices Forecasting Using Artificial Neural Networks

This paper presents specifically how artificial neural network (ANN) is applied to forecast day-ahead electricity price in the deregulated electricity market. Market clearing price (MCP) forecasting has been more and more significant in new restructured market because both generating companies and consumers rely on it to prepare their bidding strategies and expect to maximize respective benefits with low risks. But the prediction of MCP is complex because various uncertainties interact in an intricate way. Hence, ANN is proposed to solve high complicated nonlinear problems like MCP forecasting due to its powerful capability of learning mechanism if enough data for training. The paper focuses on influences brought by different input architectures with different training methods and proposes a basic optimal ANN architecture for day-ahead MCP forecasting.