| 基于支持向量机回归组合模型的中长期降温负荷预测 |
| |
| 引用本文： | 王宁,谢敏,邓佳梁,刘明波,李嘉龙,王一,刘思捷.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. |
| |
| 作者姓名： | 王宁 谢敏 邓佳梁 刘明波 李嘉龙 王一 刘思捷 |
| |
| 作者单位： | 广东电网有限责任公司电力调度控制中心，广东 广州 510600,华南理工大学电力学院，广东 广州 510640,华南理工大学电力学院，广东 广州 510640,华南理工大学电力学院，广东 广州 510640,广东电网有限责任公司电力调度控制中心，广东 广州 510600,广东电网有限责任公司电力调度控制中心，广东 广州 510600,广东电网有限责任公司电力调度控制中心，广东 广州 510600 |
| |
| 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) |
| |
| 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 |
| 收稿时间： | 2015/4/15 0:00:00 |
| 修稿时间： | 7/9/2015 12:00:00 AM |
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 and LIU Sijie.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. |
| |
| Authors: | WANG Ning XIE Min DENG Jialiang LIU Mingbo LI Jialong WANG Yi and LIU Sijie |
| |
| Affiliation: | 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,School of Electric Power, South China University of Technology, Guangzhou 510640, China,School of Electric Power, South China University of Technology, Guangzhou 510640, China,Power Dispatch and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China,Power Dispatch and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China and Power Dispatch and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, 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. |
| |
| Keywords: | support vector machine multiple linear regression nonlinear regression combined model mid-long term temperature-lowering load forecasting |
|
| 点击此处可从《电力系统保护与控制》浏览原始摘要信息 |
|
点击此处可从《电力系统保护与控制》下载全文 |
|
