电力系统保护与控制2011，Vol.39Issue(7)：44-49,6.
基于贝叶斯证据框架下WLS-SVM的短期负荷预测
Short-term load forecasting based on weighted least squares support vector machine within the Bayesian evidence framework
王林川 1白波 2于奉振 1袁明哲2
作者信息
- 1. 东北电力大学电气工程学院,吉林,吉林,132012
- 2. 成都市电业局,四川,成都,610021
- 折叠
摘要
Abstract
A short-term load forecasting model and algorithm based on the weighted least squares support vector machine within the bayesian evidence framework is proposed.On the basis of pre-processing of historical data, the author analyzes the important factors of affecting the load change, and then selects the best input data as the input vector of LS-SVM training model.The optimal parameters of models can be found through three-layer bayesian evidence inference: The weight vector w and bias value b of LS-SVM can be determined in the first layer, and the hyper-parameter γ of the model can be inferred in the second layer, the hyper-parameter σ of the nuclear function finally can be determined in the third layer.To improve the robustness of the model, WLS-SVM regression model with good generalization performance is established by giving a different weight coefficient to each sample error, which further improves the prediction accuracy of the model.Applying the proposed method to short-term load of Heilongjiang power system, results show the effectiveness of the method.关键词
贝叶斯证据框架/最小二乘支持向量机/短期负荷预测/历史数据/鲁棒性Key words
bayesian evidence framework/ least squares support vector machine (LS-SVM)/ short-term load forecasting: historical data/ robustness分类
信息技术与安全科学引用本文复制引用
王林川,白波,于奉振,袁明哲..基于贝叶斯证据框架下WLS-SVM的短期负荷预测[J].电力系统保护与控制,2011,39(7):44-49,6.
