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| 摘要: |
| 支持向量机SVM(Support Vector Machines)是一种统计学习方法，将其引入电网短期负荷预测。首先，通过聚类筛选合理的历史数据构成训练样本，再将预测的平滑性和误差损失函数相结合构成问题的目标函数，采用LIBSVM算法将SVM的大规模优化问题转化为具有解析解的二次优化问题。编制了相应的软件，对某实际电网进行了短期负荷预测，取得了理想的结果。 |
| 关键词: 支持向量机 LIBSVM 损失函数 短期负荷预测 |
| DOI： |
| 分类号:TM76 |
| 基金项目: |
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| Application of SVM to power system short-term load forecast |
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YANG Jing-fei CHENG Hao-zhong
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| Abstract: |
| The statistics learning method of SVM ( Support Vector Machines ) is introduced to short-term load forecast of power system.Sample data is constituted by filtering the historical data through clustering method.The object function considers both the fitness of prediction and error loss function.The large-scale optimization problem is solved by LIBSVM method.Corresponding software was developed and used to forecast the short-term load of a practical power system ,and the final forecast error is low. |
| Key words: support vector machines,LIBSVM,loss function,short-term load forecast,kernal function, |
