Short-Term Electricity Price Forecasting Based on Novel SVM Using Artificial Fish Swarm Algorithm under Deregulated Power

In the competition paradigm of the electric power markets, both power producers and consumers need some price prediction tools in order to plan their bidding strategies. This paper studies the problem of modeling market clearing price forecasting in deregulated markets. And electricity price forecasting with support vector machines based on artificial fish swarm algorithm is provided. Except considering market clearing price (MCP) price influential factors such as previous competitive load, making-up price, competitive generating capacity etc, the past price data have been included as attributes in input parameters. Based on these influential factors, a novel optimization support vector regression (OSVR) forecasting model is presented. The proposed algorithm is more robust and reliable as compared to traditional approach and neural networks. The effectiveness of the proposed model is demonstrated with actual data taken from the Australia Power Grid, and the actual data are compared with the presented and neural networks methods.