SUPPORT VECTOR MACHINE MODEL IN ELECTRICITY LOAD FORECASTING
With the development of electronic industry, accurate load forecasting of the future electricity demand plays an important role in regional or national power system strategy management. Electricity load forecasting is difficult due to the nonlinearity of its influencing factors. Support vector machine (SVM) have been successfully applied to solve nonlinearregression and time series problems. However, the application to load forecasting is rare. In this study, a model of support vector machine is proposed to forecast electricity load. The model overcomes the disadvantages of general artificial neural network (ANN), such as it is not easy to converge, liable to trap in partial minimum and unable to optimize globally,and the generalization of the model is not good, etc. The SVM model ensured the forecasting is optimized globally.Subsequently, examples of electricity load data from Hebei Province of China are used to illustrate the performance of the proposed model. The empirical results reveal that the proposed model outperforms the general artificial neural network model, and the forecasting accuracy improved effectively. Therefore, the model provides a promising arithmetic to forecasting electricity load in power industry.
Support vector machine (SVM) electricity load forecasting artificial neural network (ANN) LIBSVM
YING-CHUN GUO DONG-XIAO NIU YAN-XU CHEN
Department of Economics and Management, North China Electric Power University, Baoding 071003, China Department of Economics and Management, North China Electric Power University, Baoding 071003, China Hebei Software Institute, Baoding 071000,China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2892-2896
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）