Electricity Load Forecasting Based on Support Vector Machines and Simulated Annealing Particle Swarm Optimization Algorithm
Short-term electricity load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day, and on the load at the same hour on the day with the same denomination in the previous week. So the accuracy of forecasting is influenced by many unpredicted 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, it is proposed a new optimal model, which is based on Stimulated Annealing Particle Swarm Optimization Algorithm (SAPSO) that combines the advantages of PSO algorithm and SA algorithm. The new algorithm is employed to choose the parameters of a SVM model. The model is proved to be able to enhance the accuracy and improved the convergence ability and reduced operation time by numerical experiment. Subsequently, examples of electricity load data from a city in China are used to illustrate the proposed SAPSO-SVM. The empirical results reveal that the proposed model outperforms the other models. Consequently, the SAPSO-SVM model provides a promising alternative for forecasting electricity load.
Electricity short-term load forecasting Particle Swarm Optimization (PSO) Simulated Annealing Algorithms SA) Support vector machine (SVM)
Jingmin Wang Yamin Zhou Xiaoyu Chen
School of Business Administration North China Electric Power University Baoding, Hebei Province, China
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）