Short Term Electricity Price Forecasting by Hybrid Mutual Information ANFIS-PSO Approach

In a competitive electricity market, an accurate short term price forecasting is essential for all the participants in market as a risk management technique.  For both spot markets and long-term contracts, price forecast is necessary to develop bidding strategies or negotiation skills in order to maximize benefit.  This paper proposes an efficient tool for short-term electricity price forecasting with a simple model and acceptable computation time by combining several intelligent methods.  Using inference, Adaptive Network-based Fuzzy Inference System (ANFIS) is used to determine the nonlinear relation between large quantities of input variables and forecasted price (output variable).  To decrease the complexity and improve the accuracy, mutual information (MI) technique is used to efficiently select the best set of input variables which have important information concerning forecasted price.  Moreover, Particle Swarm Optimization (PSO) algorithm with new strategy in choosing the particles is adopted to tune ANFIS parameters more precisely.  To evaluate the accuracy and performance, the proposed hybrid Mutual Information-ANFIS-PSO (MIAP) methodology is implemented on the real world case study of Spanish electricity market.  The results show the great potential of this proposed method in fast and accurate short-term price forecasting in comparison with some of the previous price forecasting techniques.