Electricity Load Forecasting Using Rough Set Attribute Reduction Algorithm Based on Immune Genetic Algorithm and Support Vector Machines

Abstract

Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, a new optimal model has been proposed, which integrates a traditional support vector machines (SVM) forecasting technique with the reduction attributes of rough sets (RS) based on immune genetic algorithm (IGA) to form a new forecasting model. The model is proved to be able to enhance the accuracy and search ability to the whole of the algorithm and reduce operation time by numerical experiments. Subsequently, examples of electricity load data from a city in China are used to illustrate the performance of the proposed model. The empirical results reveal that the proposed model outperforms the other models. Therefore, the model provides an effective and feasible arithmetic to forecast electricity load in power industry.