| 引用本文: | 王宁,谢敏,邓佳梁,等.基于支持向量机回归组合模型的中长期降温负荷预测[J].电力系统保护与控制,2016,44(3):92-97.[点击复制] |
| WANG Ning,XIE Min,DENG Jialiang,et al.Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression[J].Power System Protection and Control,2016,44(3):92-97[点击复制] |
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| 摘要: |
| 提出基于支持向量机回归组合模型的中长期降温负荷预测方法。其中，支持向量机模型以多种社会经济数据为输入参数，年最大降温负荷值为输出参数。在训练过程中采用网格搜索法对支持向量机回归模型参数进行优化；回归分析中，综合采用线性、二次和三次多元回归的组合模型；最后利用最优组合预测方法将二者组合。采用广东省2008~2011年实际负荷数据和社会经济数据为训练样本，2012~2014年数据为测试样本，对支持向量机回归组合预测模型进行验证，同时也对2015和2020年最大降温负荷进行预测。结果表明，预测值与真实值的误差控制在5%以下，验证了该中长期降温负荷预测模型的有效性。目前该预测模型已在广东电网得到实际应用。 |
| 关键词: 支持向量机 多元线性回归 多项式回归 组合模型 中长期降温负荷预测 |
| DOI：10.7667/PSPC150630 |
| 投稿时间：2015-04-15修订日期：2015-07-09 |
| 基金项目:国家自然科学基金青年基金资助项目(50907023)；中国南方电网有限责任公司科技项目(K-GD2012-006) |
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| Mid-long term temperature-lowering load forecasting based on combination of support vector machine and multiple regression |
| WANG Ning,XIE Min,DENG Jialiang,LIU Mingbo,LI Jialong,WANG Yi,LIU Sijie |
| (Power Dispatch and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China;School of Electric Power, South China University of Technology, Guangzhou 510640, China) |
| Abstract: |
| A mid-long term temperature-lowering load forecasting method based on support vector machine (SVM) and multiple regression is proposed. A variety of socio-economic data is taken as the input parameter of the SVM model and the maximum temperature-lowering load as the output parameter. Grid search algorithm is used to optimize the parameters of SVM; liner, quadratic and cubic regression are used in multiple regression; finally, the two methods are integrated using optimal combined forecasting method. The SVM and multiple regression model is tested using 2008-2011 data as training sample, and 2012-2014 data as test sample. The 2015 and 2020 annual maximum temperature-lowing load are forecasted as well. The result shows that the error between the predicted value and the real value can be kept in 5%, which shows the model is effective to do mid-long term temperature-lowering load forecasting. Currently, the prediction model has been applied in Guangdong power grid. |
| Key words: support vector machine multiple linear regression nonlinear regression combined model mid-long term temperature-lowering load forecasting |
