Forecasting of Electricity Prices with Cluster Analysis

A new model of electricity price forecasting based on cluster analysis is proposed. The complex forecasting problem is divided into simpler problems in the presented model. The whole input space is partitioned into several disjointed regions. Then, support vector machine is used for modeling and forecasting for each region. In the process of cluster analysis, K-means algorithm is used for further optimizing after the number of partitioned regions and initial cluster centers are automatically obtained by using subtractive clustering method. The simulation research using the historical data from PJM market shows that the proposed model can improve the precision of electricity price forecasting effectively and stably.