摘要
提出了结合遗传算法(Genetic Algorithm,GA)和最小二乘支持向量机(Least Squares Support Vector Machines,LS-SVM)的短期电力负荷预测。由于影响负荷预测因素的复杂性和最小二乘支持向量机参数选择的不确定性,提出了采用遗传算法同时对电力负荷训练样本进行特征提取和最小二乘支持向量机的参数选择,然后利用提取出的数据序列和选择的参数,建立最小二乘支持向量机预测模型。通过实际算例分析,证明了该算法可以改善预测模型的精度和泛化能力。
A short-term load forecasting method that is based on genetic algorithm （GA） and least squares support vector machines （LS-SVM） is proposed. Because there are various factors impacting the accuracy of load forecasting and uncertain parameters to LS-SVM, the paper proposes to select features and parameters using genetic algorithm. Then, the Ls-svM model is build based on the selected features and parameters. Simulation results show that the algorithm can improve prediction model accuracy and generalization ability.
出处
《电脑开发与应用》
2013年第3期38-41,共4页
Computer Development & Applications
基金
中国青年基金重点项目(2012QNA01)
关键词
电力系统
遗传算法
最小二乘支持向量机
特征提取
短期负荷预测
power system, genetic algorithm, least squares support vector machines, feature selection, short-term load forecasting
