| 基于支持向量机回归的电力负荷预测研究 |
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| 引用本文： | 张前进. 基于支持向量机回归的电力负荷预测研究[J]. 航空计算技术, 2006, 36(4): 105-107,111 |
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| 作者姓名： | 张前进 |
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| 作者单位： | 佛山科学技术学院,自动化系,广东,佛山,528000;西北工业大学,自动化学院,陕西,西安,710072 |
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| 摘 要： | 不同于传统的基于经验风险最小化的回归方法,支持向量机回归方法基于结构风险最小化准则.与神经网络相比,该方法在解决学习精度和推广性之间的矛盾方面有明显的优势.本文以城市电力负荷预测为应用背景,对比研究了基于统计学习理论的支持向量机回归方法和神经网络方法.预测结果显示支持向量机可能是一种非常有前景的预测工具,其预测精度明显好于神经网络.
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| 关 键 词： | 结构风险最小化 支持向量机 支持向量回归 电力负荷预测 神经网络 |
| 文章编号： | 1671-654X(2006)04-0105-03 |
| 修稿时间： | 2006-03-06 |
Research on Electric-Power Load Forecasting Based on Support Vector Machine Regression Technique |
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| ZHANG Qian-jin. Research on Electric-Power Load Forecasting Based on Support Vector Machine Regression Technique[J]. Aeronautical Computer Technique, 2006, 36(4): 105-107,111 |
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| Authors: | ZHANG Qian-jin |
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| Abstract: | Based on the Statistical Learning Theory,a novel regression technique called Support Vector Machine(SVM),is explored in this paper for electric-power load forecasting.SVM is based on the principle of Structure Risk Minimization as opposed to the principle of Empirical Risk Minimization supported by conventional regression techniques.The prediction result shows that the prediction accuracy of SVM is better than that of neural network.Thus,SVM appears to be a very promising prediction tool. |
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| Keywords: | structure risk minimization support vector machine support vector regression electric-power load forecasting neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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