| 基于最小二乘支持向量机和负荷密度指标法的配电网空间负荷预测 |
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| 引用本文： | 周湶,孙威,任海军,张昀,孙才新,谢国勇,邓景云.基于最小二乘支持向量机和负荷密度指标法的配电网空间负荷预测[J].电网技术,2011(1):66-71. |
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| 作者姓名： | 周湶 孙威 任海军 张昀 孙才新 谢国勇 邓景云 |
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| 作者单位： | 输配电装备及系统安全与新技术国家重点实验室(重庆大学);四川华油集团有限责任公司; |
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| 基金项目： | 国家自然科学基金项目(50607023); 重庆市自然科学基金项目(2006BB2189)~~ |
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| 摘 要： | 传统的负荷密度指标的求取方法通常采用经验法或简单类比法,难以满足精度要求,从负荷密度与其影响因素存在着某种非线性关系的角度出发,提出了一种基于最小二乘支持向量机(least squares support vector machine,LS-SVM)的配电网空间负荷预测方法。该方法首先引入模糊C–均值算法把各类用地性质负荷聚类为几个等级,建立比较精确的负荷密度指标体系;然后根据待预测地块的规划属性,在体系中为LS-SVM预测模型选出与预测样本特征更为相似的样本进行训练,提高LS-SVM的泛化能力和预测精度;采用遗传算法对LS-SVM预测模型的参数进行自动优化,进一步提高预测模型的适应性和预测精度,实例验证了该方法的实用性和有效性。
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| 关 键 词： | 空间负荷预测 负荷密度指标法 支持向量机 模糊C–均值聚类 遗传算法 |
Spatial Load Forecasting of Distribution Network Based on Least Squares Support Vector Machine and Load Density Index System |
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| ZHOU Quan,SUN Wei,REN Haijun,ZHANG Yun,SUN Caixin,XIE Guoyong,DENG Jingyun,Shapingba District,Chongqing ,China,.Sichuan Huayou Group Co.Ltd.,Chengdu ,Sichuan Province,China.Spatial Load Forecasting of Distribution Network Based on Least Squares Support Vector Machine and Load Density Index System[J].Power System Technology,2011(1):66-71. |
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| Authors: | ZHOU Quan SUN Wei REN Haijun ZHANG Yun SUN Caixin XIE Guoyong DENG Jingyun Shapingba District Chongqing China Sichuan Huayou Group CoLtd Chengdu Sichuan Province China |
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| Affiliation: | ZHOU Quan1,SUN Wei1,REN Haijun1,ZHANG Yun1,SUN Caixin1,XIE Guoyong2,DENG Jingyun1(1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology(Chongqing University),Shapingba District,Chongqing 400030,China,2.Sichuan Huayou Group Co.Ltd.,Chengdu 610017,Sichuan Province,China) |
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| Abstract: | Empirical method or simple analogy method are often adopted in traditional methods to obtain load density index,however it is hard to meet the demand of precision.From the viewpoint that there is a certain nonlinear relation between load density and its impacting factors,a spatial load forecasting(SLF) method based on least squares support vector machine(LS-SVM) for distribution network is proposed.Firstly,the fuzzy C-means(FCM) method is led into the proposed method to cluster the land usage loads into sev... |
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| Keywords: | spatial load forecasting load density method support vector machine fuzzy C-means clustering genetic algorithm |
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