基于支持向量机和互联网信息修正的空间负荷预测方法
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摘要
为提高电网规划阶段的空间负荷预测精度,提出了一种基于支持向量机和互联网信息修正的空间负荷预测(spatialloadforecasting,SLF)方法,该方法分为3个步骤:一是基于k-均值聚类分析和支持向量回归模型得到地块负荷初始预测值;二是基于地块负荷历史数据计算负荷实际值与初始预测值之间的偏差;三是针对这些偏差,利用搜索引擎获取互联网信息,识别造成偏差的不确定事件,包括元胞中新增大负荷事件和元胞中企业营收增长率突变事件。定性分析事件对空间负荷的影响,并建立这两类事件与其造成的影响之间的分类事件影响定量模型,基于该模型对地块负荷初始预测值进行修正,得到规划区域内的地块负荷预测值。通过对北京某地区进行算例验证,结果表明该方法可以提高预测精度,可用于配电网以及能源互联网规划中的空间负荷预测。
In order to improve the spatial load forecasting accuracy of power system planning, a spatial load forecasting(SLF)method is proposed based on support vector machine(SVM) and Internet information correction. The method can be divided into three steps: firstly, based on k-means clustering analysis, the SVM model is used to get the initial prediction value of the block load;secondly, the deviation between the actual load value and the predicted load value can be calculated based on the historical data of the block load; and thirdly, those uncertain events that causes deviation, including the newly increasing load events in the cells and the mutation of revenue growth rate of the enterprises in the cells, are identified by Internet information that is obtained by search engine. The impact of the uncertain events on spatial load is qualitatively analyzed, and a quantitative impact model is established for classifying the events between these two events and their effects. Based on the model, the initial forecasting value of the load is corrected, and the final forecasting load values of the blocks in the planning area is obtained. A case study of an area in Beijing shows that this method can improve the prediction accuracy and can be used for spatial load forecasting in distribution network and energy Internet planning.
In order to improve the spatial load forecasting accuracy of power system planning, a spatial load forecasting(SLF)method is proposed based on support vector machine(SVM) and Internet information correction. The method can be divided into three steps: firstly, based on k-means clustering analysis, the SVM model is used to get the initial prediction value of the block load;secondly, the deviation between the actual load value and the predicted load value can be calculated based on the historical data of the block load; and thirdly, those uncertain events that causes deviation, including the newly increasing load events in the cells and the mutation of revenue growth rate of the enterprises in the cells, are identified by Internet information that is obtained by search engine. The impact of the uncertain events on spatial load is qualitatively analyzed, and a quantitative impact model is established for classifying the events between these two events and their effects. Based on the model, the initial forecasting value of the load is corrected, and the final forecasting load values of the blocks in the planning area is obtained. A case study of an area in Beijing shows that this method can improve the prediction accuracy and can be used for spatial load forecasting in distribution network and energy Internet planning.
引文
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[2]肖白,周潮,穆钢.空间电力负荷预测方法综述与展望[J].中国电机工程学报,2013, 33(25):78-92.XIAO Bai, ZHOU Chao, MU Gang. Review and prospect of the spatial load forecasting methods[J]. Proceedings of the CSEE, 2013,33(25):78-92.
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[5] CHOW M Y, TRAM H. Application of fuzzy logic technology for spatial load forecasting[J]. IEEE Transactions on Power Systems,1997, 12(3):1360-1366.
[6] WU H C, LU C N. A data mining approach for spatial modeling in small area load forecast[J]. IEEE Transactions on Power Systems,2002, 17(2):516-521.
[7] CHOU M Y, ZHU J, TRAM H. Application of fuzzy multi-objective decision making in spatial load forecasting[J]. IEEE Transactions on Power Systems,1998, 13(13):1185-1190.
[8]余建明,燕飞,杨文宇,等.基于模糊多目标多人决策的配电网空间负荷预测[J].电网技术,2006, 30(7):48-52.YU Jianming, YAN Fei, YANG Wenyu, et al. Spatial load forecasting of distribution network based on fuzzy multi-objective multi-person decision making[J]. Power System Technology, 2006,30(7):48-52.
[9]雷绍兰,孙才新,周湶,等.模糊粗糙集理论在空间电力负荷预测中的应用[J].电网技术,2005, 29(9):26-30.LEI Shaolan, SUN Caixin, ZHOU Quan, et al. Application of fuzzy rough set theory in spatial load forecasting[J]. Power System Technology, 2005, 29(9):26-30.
[10]杨薛明,苑津莎,王剑锋,等.基于云理论的配电网空间负荷预测方法研究[J].中国电机工程学报,2006, 26(6):30-36.YANG Xueming, YUAN Jinsha, WANG Jianfeng, et al. A new spatial forecasting method for distribution network based on cloud theory[J]. Proceedings of the CSEE, 2006, 26(6):30-36.
[11]周湶,李健,孙才新,等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008, 28(25):68-73.ZHOU Quan, LI Jian, SUN Caixin, et al. Spatial load forecasting for distribution networks based on rough sets and cellular automata[J].Proceedings of the CSEE,2008, 28(25):68-73.
[12]刘自发,庞铖铖,王泽黎,等.基于云理论和元胞自动机理论的城市配电网空间负荷预测[J].中国电机工程学报,2013, 33(10):98-105.LIU Zifa, PANG Chengcheng, WANG Zeli, et al. Spatial load forecasting for distribution network based on cloud theory and cellular automata[J]. Proceedings of the CSEE, 2013, 33(10):98-105.
[13]杨丽徙,王金风,陈根永,等.基于元胞自动机理论的电力负荷空间分布预测[J].中国电机工程学报,2007, 27(4):15-20.YANG Lixi, WANG Jinfeng, CHEN Genyong, et al. Load spatial distribution forecasting model on cellular automata theory[J].Proceedings of the CSEE, 2007, 27(4):15-20.
[14] CARRENO E M, ROCHA R M, PADILHA-FELTRIN A. A cellular automaton approach to spatial electric load forecasting[J]. IEEE Transactions on Power Systems, 2011, 26(2):532-540.
[15]钟庆,吴捷,伍力,等.基于系统动力学的分区负荷预测[J].电网技术,2001,25(3):51-55.ZHONG Qing, WU Jie, WU Li, et al. Subarea load forecasting basedon system dynamics[J]. Power System Technology, 2001, 25(3):51-55.
[16]陶文斌,张粒子,潘弘,等.基于双层贝叶斯分类的空间负荷预测[J].中国电机工程学报,2007, 27(7):13-17.TAO Wenbin, ZHANG Lizi, PAN Hong, et al. Spatial electric load forecasting based on double-level Bayesian classification[J].Proceedings of the CSEE,2007, 27(7):13-17.
[17]刘自发,庞铖铖,魏建炜,等.基于IAHP和TOPSIS方法的负荷密度指标计算[J].电力系统自动化,2012, 36(13):56-59.LIU Zifa, PANG Chengcheng, WEI Jianwei, et al. Index calculation of load density based on IAHP and TOPSIS methods[J]. Automation of Electric Power Systems, 2012, 36(13):56-59.
[18]符杨,曹家麟,谢楠,等.基于模糊综合评判的负荷密度指标选取新方法[J].电网技术,2007, 31(18):19-22.FU Yang, CAO Jialin, XIE Nan, et al. A novel fuzzy comprehensive evaluation based method to select load density and index[J]. Power System Technology,2007, 31(18):19-22.
[19]吴斌,陈章潮,包海龙.基于人工神经元网络及模糊算法的空间负荷预测[J].电网技术,1999, 23(11):1-4.WU Bin, CHEN Zhangchao, BAO Hailong. Spatial electric load forecasting based on artificial neural networks and fuzzy algorithm[J]. Power System Technology, 1999, 23(11):1-4.
[20]周湶,孙威,张昀,等.基于改进型ANFIS的负荷密度指标求取新方法[J].电力系统保护与控制,2011, 39(1):29-34.ZHOU Quan, SUN Wei, ZHANG Yun, et al. A new method to obtain load density based on improved ANFIS[J]. Power System Protection and Control,2011,39(1):29-34.
[21]周湶,孙威,任海军,等.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, 35(1):66-71.ZHOU Quan, SUN Wei, REN Haijun, et al. Spatial load forecasting of distribution network based on least squares support vector machine and load density index system[J]. Power System Technology, 2011,35(1):66-71.
[22]肖白,聂鹏,穆钢,等.基于多级聚类分析和支持向量机的空间负荷预测方法[J].电力系统自动化,2015, 39(12):56-61.XIAO Bai, NIE Peng, MU Gang, et al. A spatial load forecasting method based on multilevel clustering analysis and support vector machine[J]. Automation of Electric Power Systems, 2015, 39(12):56-61.
[23]肖白,刘庆永,牛强,等.基于元胞负荷特性分析的RBF神经网络空间负荷预测方法[J].电网技术,2018, 42(1):301-307.XIAO Bai, LIU Qingyong, NIU Qiang, et al. A spatial loadforecasting method based on RBF neural network and cellular load characteristics analysis[J]. Power System Technology, 2018, 42(1):301-307.
[24]王天华,王平洋,袁钦成.空间负荷预测中不确定因素的处理方法[J].电网技术,2001,25(1):22-25.WANG Tianhua, WANG Pingyang, YUAN Qincheng. Processing of uncertainty in spatial load forecasting for distribution planning[J].Power System Technology, 2001, 25(1):22-25.
[25] MELO J D, PADILHA-FELTRIN A, CARRENO E M. Data issues in spatial electric load forecasting[C]//2014 IEEE PES General Meeting Conference&Exposition, National Harbor, MD,USA,2014:1-5.
[26]周志华.机器学习[M].北京:清华大学出版社,2016.
[27] ROUSSEEUW P J. Silhouettes:a graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics,1987, 20:53-65.
[28] ARTHUR D, VASSILVITSKII S. k-means++:the advantages of careful seeding[C]//Proceedings of the 18th Annual ACM-SIAMSymposium on Discrete Algorithms, New Orleans, LA, USA, 2007:1027-1035.
[2]肖白,周潮,穆钢.空间电力负荷预测方法综述与展望[J].中国电机工程学报,2013, 33(25):78-92.XIAO Bai, ZHOU Chao, MU Gang. Review and prospect of the spatial load forecasting methods[J]. Proceedings of the CSEE, 2013,33(25):78-92.
[3]王天华,范明天,王平洋,等.基于地理信息系统平台的配电网空间负荷预测[J].电网技术,1999, 23(5):42-47.WANG Tianhua, FAN Mingtian, WANG Pingyang, et al. Spatial load forecasting for distribution planning based on GIS platform[J].Power System Technology, 1999, 23(5):42-47.
[4]王天华,王平洋,范明天.遗传算法、模糊逻辑和运输模型在配电网空间负荷预测中的应用[J].电网技术,1999, 23(1):24-28.WANG Tianhua, WANG Pingyang, FAN Mingtian. Application of genetic algorithm, fuzzy logic and transshipment model to spatial load forecasting for distribution planning[J]. Power System Technology, 1999, 23(1):24-28.
[5] CHOW M Y, TRAM H. Application of fuzzy logic technology for spatial load forecasting[J]. IEEE Transactions on Power Systems,1997, 12(3):1360-1366.
[6] WU H C, LU C N. A data mining approach for spatial modeling in small area load forecast[J]. IEEE Transactions on Power Systems,2002, 17(2):516-521.
[7] CHOU M Y, ZHU J, TRAM H. Application of fuzzy multi-objective decision making in spatial load forecasting[J]. IEEE Transactions on Power Systems,1998, 13(13):1185-1190.
[8]余建明,燕飞,杨文宇,等.基于模糊多目标多人决策的配电网空间负荷预测[J].电网技术,2006, 30(7):48-52.YU Jianming, YAN Fei, YANG Wenyu, et al. Spatial load forecasting of distribution network based on fuzzy multi-objective multi-person decision making[J]. Power System Technology, 2006,30(7):48-52.
[9]雷绍兰,孙才新,周湶,等.模糊粗糙集理论在空间电力负荷预测中的应用[J].电网技术,2005, 29(9):26-30.LEI Shaolan, SUN Caixin, ZHOU Quan, et al. Application of fuzzy rough set theory in spatial load forecasting[J]. Power System Technology, 2005, 29(9):26-30.
[10]杨薛明,苑津莎,王剑锋,等.基于云理论的配电网空间负荷预测方法研究[J].中国电机工程学报,2006, 26(6):30-36.YANG Xueming, YUAN Jinsha, WANG Jianfeng, et al. A new spatial forecasting method for distribution network based on cloud theory[J]. Proceedings of the CSEE, 2006, 26(6):30-36.
[11]周湶,李健,孙才新,等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008, 28(25):68-73.ZHOU Quan, LI Jian, SUN Caixin, et al. Spatial load forecasting for distribution networks based on rough sets and cellular automata[J].Proceedings of the CSEE,2008, 28(25):68-73.
[12]刘自发,庞铖铖,王泽黎,等.基于云理论和元胞自动机理论的城市配电网空间负荷预测[J].中国电机工程学报,2013, 33(10):98-105.LIU Zifa, PANG Chengcheng, WANG Zeli, et al. Spatial load forecasting for distribution network based on cloud theory and cellular automata[J]. Proceedings of the CSEE, 2013, 33(10):98-105.
[13]杨丽徙,王金风,陈根永,等.基于元胞自动机理论的电力负荷空间分布预测[J].中国电机工程学报,2007, 27(4):15-20.YANG Lixi, WANG Jinfeng, CHEN Genyong, et al. Load spatial distribution forecasting model on cellular automata theory[J].Proceedings of the CSEE, 2007, 27(4):15-20.
[14] CARRENO E M, ROCHA R M, PADILHA-FELTRIN A. A cellular automaton approach to spatial electric load forecasting[J]. IEEE Transactions on Power Systems, 2011, 26(2):532-540.
[15]钟庆,吴捷,伍力,等.基于系统动力学的分区负荷预测[J].电网技术,2001,25(3):51-55.ZHONG Qing, WU Jie, WU Li, et al. Subarea load forecasting basedon system dynamics[J]. Power System Technology, 2001, 25(3):51-55.
[16]陶文斌,张粒子,潘弘,等.基于双层贝叶斯分类的空间负荷预测[J].中国电机工程学报,2007, 27(7):13-17.TAO Wenbin, ZHANG Lizi, PAN Hong, et al. Spatial electric load forecasting based on double-level Bayesian classification[J].Proceedings of the CSEE,2007, 27(7):13-17.
[17]刘自发,庞铖铖,魏建炜,等.基于IAHP和TOPSIS方法的负荷密度指标计算[J].电力系统自动化,2012, 36(13):56-59.LIU Zifa, PANG Chengcheng, WEI Jianwei, et al. Index calculation of load density based on IAHP and TOPSIS methods[J]. Automation of Electric Power Systems, 2012, 36(13):56-59.
[18]符杨,曹家麟,谢楠,等.基于模糊综合评判的负荷密度指标选取新方法[J].电网技术,2007, 31(18):19-22.FU Yang, CAO Jialin, XIE Nan, et al. A novel fuzzy comprehensive evaluation based method to select load density and index[J]. Power System Technology,2007, 31(18):19-22.
[19]吴斌,陈章潮,包海龙.基于人工神经元网络及模糊算法的空间负荷预测[J].电网技术,1999, 23(11):1-4.WU Bin, CHEN Zhangchao, BAO Hailong. Spatial electric load forecasting based on artificial neural networks and fuzzy algorithm[J]. Power System Technology, 1999, 23(11):1-4.
[20]周湶,孙威,张昀,等.基于改进型ANFIS的负荷密度指标求取新方法[J].电力系统保护与控制,2011, 39(1):29-34.ZHOU Quan, SUN Wei, ZHANG Yun, et al. A new method to obtain load density based on improved ANFIS[J]. Power System Protection and Control,2011,39(1):29-34.
[21]周湶,孙威,任海军,等.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, 35(1):66-71.ZHOU Quan, SUN Wei, REN Haijun, et al. Spatial load forecasting of distribution network based on least squares support vector machine and load density index system[J]. Power System Technology, 2011,35(1):66-71.
[22]肖白,聂鹏,穆钢,等.基于多级聚类分析和支持向量机的空间负荷预测方法[J].电力系统自动化,2015, 39(12):56-61.XIAO Bai, NIE Peng, MU Gang, et al. A spatial load forecasting method based on multilevel clustering analysis and support vector machine[J]. Automation of Electric Power Systems, 2015, 39(12):56-61.
[23]肖白,刘庆永,牛强,等.基于元胞负荷特性分析的RBF神经网络空间负荷预测方法[J].电网技术,2018, 42(1):301-307.XIAO Bai, LIU Qingyong, NIU Qiang, et al. A spatial loadforecasting method based on RBF neural network and cellular load characteristics analysis[J]. Power System Technology, 2018, 42(1):301-307.
[24]王天华,王平洋,袁钦成.空间负荷预测中不确定因素的处理方法[J].电网技术,2001,25(1):22-25.WANG Tianhua, WANG Pingyang, YUAN Qincheng. Processing of uncertainty in spatial load forecasting for distribution planning[J].Power System Technology, 2001, 25(1):22-25.
[25] MELO J D, PADILHA-FELTRIN A, CARRENO E M. Data issues in spatial electric load forecasting[C]//2014 IEEE PES General Meeting Conference&Exposition, National Harbor, MD,USA,2014:1-5.
[26]周志华.机器学习[M].北京:清华大学出版社,2016.
[27] ROUSSEEUW P J. Silhouettes:a graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics,1987, 20:53-65.
[28] ARTHUR D, VASSILVITSKII S. k-means++:the advantages of careful seeding[C]//Proceedings of the 18th Annual ACM-SIAMSymposium on Discrete Algorithms, New Orleans, LA, USA, 2007:1027-1035.