| 基于最小二乘支持向量机的短期电力负荷预测 |
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| 引用本文： | 曹彦,王倩,周驰.基于最小二乘支持向量机的短期电力负荷预测[J].电脑开发与应用,2013(3):38-41. |
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| 作者姓名： | 曹彦 王倩 周驰 |
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| 作者单位： | 周口师范学院计算机科学与技术学院;许昌供电公司 |
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| 基金项目： | 中国青年基金重点项目(2012QNA01) |
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| 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。
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| 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 |
Short-term Load Forecasting Based on Least Squares Support Vector Machines |
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| CAO Yan,WANG Qian,ZHOU Chi.Short-term Load Forecasting Based on Least Squares Support Vector Machines[J].Computer Development & Applications,2013(3):38-41. |
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| Authors: | CAO Yan WANG Qian ZHOU Chi |
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| Affiliation: | 1.Zhoukou Normal University,Zhoukou 466001,China,2.Xuchang Power Supply Company,Xuchang 461000,China) |
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| Abstract: | 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. |
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| Keywords: | power system genetic algorithm least squares support vector machines feature selection short-term load forecasting |
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