| 基于稀疏核偏最小二乘法的短期负荷预测研究 |
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| 引用本文： | 谷松林,秦志明. 基于稀疏核偏最小二乘法的短期负荷预测研究[J]. 华北电力大学学报(自然科学版), 2010, 37(2) |
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| 作者姓名： | 谷松林 秦志明 |
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| 作者单位： | 华北电力大学,电气与电子工程学院;华北电力大学,能源与动力工程学院,河北,保定,071003 |
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| 摘 要： | 支持向量机方法已成功地应用在负荷预测领域,但它在训练数据时存在数据处理量太大、处理速度慢等缺点。为此提出了一种基于稀疏核偏最小二乘法的预测方法,通过在高维特征空间的稀疏化,可减少在训练过程中的数据量,从而提高预测的速度和精度,将该方法应用于短期负荷预测中,与SVM方法相比,得到了较高的预测精度。
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| 关 键 词： | 电力系统 短期负荷预测 核函数 稀疏化 偏最小二乘法 |
Study on short-termload forecasting based on sparse kernel partialaleast squares |
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| GU Song-lin,QIN Zhi-ming. Study on short-termload forecasting based on sparse kernel partialaleast squares[J]. Journal of North China Electric Power University, 2010, 37(2) |
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| Authors: | GU Song-lin QIN Zhi-ming |
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| Affiliation: | a.School of Electrical and Electronic Engineering;b.School of Energy and Power Engineering;North China Electric Power University;Baoding 071003;China |
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| Abstract: | The support vector machine(SVM) has been successfully applied to the load forecasting area,but it has some disadvantages of very large data amount and slow processing speed.A better method of sparse kernel partial least squares was proposed,the data amount is decreased in the training process by sparse in high dimension feature space,and the processing speed was improved. The simulation result shows that the algorithm has a better forecasting accuracy compared with the method of SVM. |
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| Keywords: | power system short-term load forecasting kernel method sparse partial least squares |
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