| 基于GA的改进SVM算法对RBF优化算法在短期负荷预测中的应用 |
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| 引用本文： | 张红,张建红,康岩松.基于GA的改进SVM算法对RBF优化算法在短期负荷预测中的应用[J].长春工程学院学报(自然科学版),2011,12(2):21-25. |
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| 作者姓名： | 张红 张建红 康岩松 |
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| 作者单位： | 长春工程学院电气与信息学院,长春,130012;吉电股份有限公司四平热电公司,四平,255400 |
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| 基金项目： | 吉林省教育厅科研项目(2009259) |
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| 摘 要： | 高预测精度的短期负荷预测对于坚强电网非常重要,根据电力负荷特性的变化规律,提出了一种改进的基于径向基函数神经网络的短期负荷预测方法,应用经GA优化的SVM多核径向基函数去提取有用数据,提高了基于RBF神经网络的短期负荷预测精度.以美国加州春季负荷为输入数据,应用MATLAB仿真说明改进算法的优越性和其鲁棒性.
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| 关 键 词： | RBF神经网络 短期负荷预测 支持向量机 遗传算法 |
The application of improved SVM algorithm to RBF optimal algorithm in the short-term load forecasting based on GA |
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| ZHANG Hong,etc..The application of improved SVM algorithm to RBF optimal algorithm in the short-term load forecasting based on GA[J].Journal of Changchun Institute of Technology(Natural Science Edition),2011,12(2):21-25. |
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| Authors: | ZHANG Hong etc |
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| Institution: | ZHANG Hong,etc.(Faulty of Electrical & Information Engineering,Changchun Insitute of Technology,Changchun 130012,China) |
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| Abstract: | High precision short-term load forecasting is very important to the strong power gird.In this paper,an improved Radial Basis Function(RBF) neural network short-term load forecasting method is proposed,which is according to variable law of power load characteristics.Using multi-core GA-optimized radial basis function SVM to extract useful data,it improves the accuracy of short-term load forecasting based RBF neural net work.The robustness and advantages of this improved forecasting strategy is demonstrated w... |
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| Keywords: | RBF neural network short-term load forecasting support vector machine genetic algorithm |
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