The Application of Joint Optimization Method using GA for Load Forecasting

Abstract

Abstract. Since load forecasting plays an important role in the planning and operation of power industry, substantial efforts are made in improving the accuracy and reliability of load forecasting. In this paper, we develop a novel hybrid approach based on phase space reconstruction and least square support vector for the short-term load forecasting. However, the proper parameters in phase space reconstruction and least square vector machine have a significant effect on the forecasting performance, and there is no standard solution for the parameter estimation problem. Therefore, in this paper, the genetic algorithm (GA) approach is employed to optimize the parameters of both phase space reconstruction and least square support vector machine together. The experimental results suggest that the joint optimization parameter is superior to the separate optimization solutions.