Mid-term electricity market clearing price forecasting using multiple least squares support vector machines

Mid-term electricity market clearing price (MCP) forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. A multiple least squares support vector machine (LSSVM) based mid-term electricity MCP forecasting model is proposed in this study. Data classification and price forecasting modules are designed to first pre-process the input data into corresponding price zones, and then forecast the electricity price. The proposed model showed improved forecasting accuracy on both peak prices and overall system compared to the forecasting model using a single LSSVM. PJM interconnection data are used to test the proposed model.