A New Method for Load Forecasting Based on PCA-LSSVM
Load forecasting plays a key role in power system operation and planning. However, the influencing factors of electric power load are very complex and various. To achieve higher precision, as many of factors as possible are input in the forecast model with complex computing as its cost. Principal components analysis (PCA) is one of multivariate statistic analysis, which achieves parsimony and reduction dimensionality to simplify computing by extracting the smallest number of irrelevant components with little loss of information. In this paper, a new method for short-term load forecasting based on PCA and least squares support vector machines (LS-SVM) is presented. Firstly, principal components are extracted from various factors of load by PCA to be inputs of LS-SVM. Then LS-SVM is applied to train and predict. The model is characterized by all-sided influencing factors and simple computing. Analysis of the experimental results proved that the method proposed achieves greater accuracy and efficiency than conventional LS-SVM.
load forecasting influencing factors principal component analysis (PCA) least squares support vector machine (LS-SVM)1
Liu Baoying Yang Rengang
College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
国际会议
广州
英文
2007-11-10（万方平台首次上网日期，不代表论文的发表时间）