基于协整-格兰杰因果检验和季节分解的中期负荷预测
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摘要
近年来,随着国民经济的转型,中国的经济结构发生了较大的变化,仅仅依靠电力负荷历史数据进行负荷电量预测会造成较大的误差。为解决传统负荷预测方法对于经济、气象等因素考虑不足的问题,提出了一种可以计及经济与气象等因素影响的中期负荷电量预测方法。首先利用季节分解将历史月度用电量分解为长期趋势及循环分量、季节分量以及不规则分量;并以计量经济学中的协整检验以及格兰杰因果检验分析经济因素与用电量长期趋势及循环分量的关系,确定影响该部分电量预测的关键性指标;基于电量、气象以及经济数据,对各个分量利用支持向量机分别进行预测并综合得到月度电量总量预测值;最后通过算例分析了方法的有效性与可行性。
In recent years,with the transformation of national economy,great changes have taken place in the economic structure of China.The prediction based on the historical data of electric power load will cause great error.In order to solve the problem which traditional load forecasting method is not enough for economic and meteorological factors,a forecasting method for medium-term load is proposed.This method can consider the influence of economy,climate and other factors.First,using seasonal decomposition,the monthly electricity consumption of history is decomposed into long-term and cycle component,seasonal component and irregular component,and the relationship between economic factors and long-term trend and cyclic components of electricity consumption is analyzed by cointegration test and Granger causality test in econometrics.The key indexes to influence the prediction of electric quantity is determined.Each component is predicted by support vector machine(SVM)based on electricity,meteorology and economic data,and the monthly total quantity of electricity is predicted.Finally,the effectiveness and feasibility of the method are illustrated by an example.
In recent years,with the transformation of national economy,great changes have taken place in the economic structure of China.The prediction based on the historical data of electric power load will cause great error.In order to solve the problem which traditional load forecasting method is not enough for economic and meteorological factors,a forecasting method for medium-term load is proposed.This method can consider the influence of economy,climate and other factors.First,using seasonal decomposition,the monthly electricity consumption of history is decomposed into long-term and cycle component,seasonal component and irregular component,and the relationship between economic factors and long-term trend and cyclic components of electricity consumption is analyzed by cointegration test and Granger causality test in econometrics.The key indexes to influence the prediction of electric quantity is determined.Each component is predicted by support vector machine(SVM)based on electricity,meteorology and economic data,and the monthly total quantity of electricity is predicted.Finally,the effectiveness and feasibility of the method are illustrated by an example.
引文
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[14]吴倩红,高军,侯广松,等.实现影响因素多源异构融合的短期负荷预测支持向量机算法[J].电力系统自动化,2016,40(15):67-72.DOI:10.7500/AEPS20160229012.WU Qianhong,GAO Jun,HOU Guangsong,et al.Short-term load forecasting support vector machine algorithm based on multi-source heterogeneous fusion of load factors[J].Automation of Electric Power Systems,2016,40(15):67-72.DOI:10.7500/AEPS20160229012.
[15]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.DOI:10.7500/AEPS20140520001.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.DOI:10.7500/AEPS20140520001.
[16]吴潇雨,和敬涵,张沛,等.基于灰色投影改进随机森林算法的电力系统短期负荷预测[J].电力系统自动化,2015,39(12):50-55.DOI:10.7500/AEPS20140916005.WU Xiaoyu,HE Jinghan,ZHANG Pei,et al.Power system short-term load forecasting based on improved random forest with grey relation projection[J].Automation of Electric Power Systems,2015,39(12):50-55.DOI:10.7500/AEPS20140916005.
[17]郭鸿业,陈启鑫,夏清,等.考虑经济因素时滞效应的月度负荷预测方法[J].电网技术,2016,40(2):514-520.GUO Hongye,CHEN Qixin,XIA Qing,et al.Study on midterm electricity load forecast considering time lag effects of economic factors[J].Power System Technology,2016,40(2):514-520.
[18]任丽娜.基于Elman神经网络的中期电力负荷预测模型研究[D].兰州:兰州理工大学,2007.REN Lina.Research on medium-term electrical load forecasting model based on Elman neural network[D].Lanzhou:Lanzhou University of Technology,2007.
[19]中国人民银行调查统计司.时间序列X-12-ARIMA季节调整---原理与方法[M].北京:中国金融出版社,2006:121-144.Investigation and Statistics Department of the People’s Bank of China.X-12-ARIMA seasonal adjustment of time seriesprinciples and methods[M].Beijing:China Finance Publishing House,2006:121-144.
[20]颜伟,程超,薛斌,等.结合X12乘法模型和ARIMA模型的月售电量预测方法[J].电力系统及其自动化学报,2016,28(5):74-80.YAN Wei,CHENG Chao,XUE Bin,et al.Forecasting for monthly electricity consumption using X12 multiplication method and ARIMA model[J].Proceedings of the CSU-EPSA,2016,28(5):74-80.
[21]潘泽清.时间序列分析---宏观经济数据分析模型[M].北京:经济科学出版社,2017:121-145.PAN Zeqing.Time series analysis-macroeconomic data analysis model[M].Beijing:Economic Science Press,2017:121-145.
[22]周会友.基于支持向量机的风电场风速预测方法研究[D].华北电力大学(北京),2017.ZHOU Huiyou.Research on wind speed forecasting method of wind farm based on support vector machine[D].North China Electric Power University(Beijing),2017.
[23]袁家海,丁伟,胡兆光.电力消费与中国经济发展的协整与波动分析[J].电网技术,2006,30(9):10-15.YUAN Jiahai,DING Wei,HU Zhaoguang.Analysis on cointegration and co-movement of electricity consumption and economic growth in China[J].Power System Technology,2006,30(9):10-15.
[24]王丰龙,刘云刚.中国城市建设用地扩张与财政收入增长的面板格兰杰因果检验[J].地理学报,2013,68(12):1595-1606.WANG Feng,Liu Yungang.Panel Granger test on urban land expansion and fiscal revenue growth in China’s prefecture-level cities[J].Acta Geographica Sinica,2013,68(12):1595-1606.
[25]PARDO A,MENEU V,VALOR E.Temperature and seasonality influences on Spanish electricity load[J].Energy Economics,2002,24(1):55-70.
[2]牛东晓,魏亚楠.基于FHNN相似日聚类自适应权重的短期电力负荷组合预测[[J].电力系统自动化,2013,37(3):54-57.NIU Dongxiao,WEI Yanan.Short-term power load combinatorial forecast adaptively weighted by FHNN similarday clustering[J].Automation of Electric Power Systems,2013,37(3):54-57.
[3]钱卫华,姚建刚,龙立波,等.基于短期相关性和负荷增长的中长期负荷预测[J].电力系统自动化,2007,31(11):59-64.QIAN Weihua,YAO Jiangang,LONG Libo,et al.Short-term correlation and annual growth based mid-long term load forecasting[J].Automation of Electric Power Systems,2007,31(11):59-64.
[4]PAPALEXOPOULOS A D,HESTERBERG T C.A regressionbased approach to short-term system load forecasting[J].IEEETransactions on Power Systems,1990,5(4):1535-1547.
[5]SONG K B,BAEK Y S,HONG D H,et al.Short-term load forecasting for the holidays using fuzzy linear regression method[J].IEEE Transactions on Power Systems,2005,20(1):96-101.
[6]张伏生,刘芳,赵文彬,等.灰色Verhulst模型在中长期负荷预测中的应用[J].电网技术,2003,27(5):37-39.ZHANG Fusheng,LIU Fang,ZHAO Wenbin,et al.Application of grey Verhulst model in middle and long term load forecasting[J].Power System Technology,2003,27(5):37-39.
[7]ABDULLAH A G.Hybrid PSO-ANN application for improved accuracy of short term load forecasting[J].IEEE Transactions on Power Systems,2014,9(2):446-451.
[8]CHARYTONIUK W,CHEN M S.Very short-term load forecasting using artificial neural networks[J].IEEETransactions on Power Systems,2002,17(1):263-268.
[9]吴云,雷建文,鲍丽山,等.基于改进灰色关联分析与蝙蝠优化神经网络的短期负荷预测[J].电力系统自动化,2018,42(20):67-72.DOI:10.7500/AEPS20180125004.WU Yun,LEI Jianwen,BAO Lishan,et al.Short-term load forecasting based on improved grey relational analysis and neural network optimized by bat algorithm[J].Automation of Electric Power Systems,2018,42(20):67-72.DOI:10.7500/AEPS20180125004.
[10]MORDJAOUI M,HADDAD S,MEDOUED A,et al.Electric load forecasting by using dynamic neural network[J].International Journal of Hydrogen Energy,2017,42(28):17655-17663.
[11]孔祥玉,郑锋,鄂志君,等.基于深度信念网络的短期负荷预测方法[J].电力系统自动化,2018,42(5):133-139.DOI:10.7500/AEPS20170826002.KONG Xiangyu,ZHENG Feng,E Zhijun,et al.Short-term load forecasting based on deep belief network[J].Automation of Electric Power Systems,2018,42(5):133-139.DOI:10.7500/AEPS20170826002.
[12]畅广辉,刘涤尘,熊浩.基于多分辨率SVM回归估计的短期负荷预测[J].电力系统自动化,2007,31(9):37-41.CHANG Guanghui,LIU Dichen,XIONG Hao.Short term load forecasting based on multi-resolution SVM regression[J].Automation of Electric Power Systems,2007,31(9):37-41.
[13]李如琦,苏浩益,王宗耀,等.应用启发式最小二乘支持向量机的中长期电力负荷预测[J].电网技术,2011,35(11):195-199.LI Ruqi,SU Haoyi,WANG Zongyao,et al.Medium-and longterm load forecasting based on heuristic least square support vector machine[J].Power System Technology,2011,35(11):195-199.
[14]吴倩红,高军,侯广松,等.实现影响因素多源异构融合的短期负荷预测支持向量机算法[J].电力系统自动化,2016,40(15):67-72.DOI:10.7500/AEPS20160229012.WU Qianhong,GAO Jun,HOU Guangsong,et al.Short-term load forecasting support vector machine algorithm based on multi-source heterogeneous fusion of load factors[J].Automation of Electric Power Systems,2016,40(15):67-72.DOI:10.7500/AEPS20160229012.
[15]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.DOI:10.7500/AEPS20140520001.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.DOI:10.7500/AEPS20140520001.
[16]吴潇雨,和敬涵,张沛,等.基于灰色投影改进随机森林算法的电力系统短期负荷预测[J].电力系统自动化,2015,39(12):50-55.DOI:10.7500/AEPS20140916005.WU Xiaoyu,HE Jinghan,ZHANG Pei,et al.Power system short-term load forecasting based on improved random forest with grey relation projection[J].Automation of Electric Power Systems,2015,39(12):50-55.DOI:10.7500/AEPS20140916005.
[17]郭鸿业,陈启鑫,夏清,等.考虑经济因素时滞效应的月度负荷预测方法[J].电网技术,2016,40(2):514-520.GUO Hongye,CHEN Qixin,XIA Qing,et al.Study on midterm electricity load forecast considering time lag effects of economic factors[J].Power System Technology,2016,40(2):514-520.
[18]任丽娜.基于Elman神经网络的中期电力负荷预测模型研究[D].兰州:兰州理工大学,2007.REN Lina.Research on medium-term electrical load forecasting model based on Elman neural network[D].Lanzhou:Lanzhou University of Technology,2007.
[19]中国人民银行调查统计司.时间序列X-12-ARIMA季节调整---原理与方法[M].北京:中国金融出版社,2006:121-144.Investigation and Statistics Department of the People’s Bank of China.X-12-ARIMA seasonal adjustment of time seriesprinciples and methods[M].Beijing:China Finance Publishing House,2006:121-144.
[20]颜伟,程超,薛斌,等.结合X12乘法模型和ARIMA模型的月售电量预测方法[J].电力系统及其自动化学报,2016,28(5):74-80.YAN Wei,CHENG Chao,XUE Bin,et al.Forecasting for monthly electricity consumption using X12 multiplication method and ARIMA model[J].Proceedings of the CSU-EPSA,2016,28(5):74-80.
[21]潘泽清.时间序列分析---宏观经济数据分析模型[M].北京:经济科学出版社,2017:121-145.PAN Zeqing.Time series analysis-macroeconomic data analysis model[M].Beijing:Economic Science Press,2017:121-145.
[22]周会友.基于支持向量机的风电场风速预测方法研究[D].华北电力大学(北京),2017.ZHOU Huiyou.Research on wind speed forecasting method of wind farm based on support vector machine[D].North China Electric Power University(Beijing),2017.
[23]袁家海,丁伟,胡兆光.电力消费与中国经济发展的协整与波动分析[J].电网技术,2006,30(9):10-15.YUAN Jiahai,DING Wei,HU Zhaoguang.Analysis on cointegration and co-movement of electricity consumption and economic growth in China[J].Power System Technology,2006,30(9):10-15.
[24]王丰龙,刘云刚.中国城市建设用地扩张与财政收入增长的面板格兰杰因果检验[J].地理学报,2013,68(12):1595-1606.WANG Feng,Liu Yungang.Panel Granger test on urban land expansion and fiscal revenue growth in China’s prefecture-level cities[J].Acta Geographica Sinica,2013,68(12):1595-1606.
[25]PARDO A,MENEU V,VALOR E.Temperature and seasonality influences on Spanish electricity load[J].Energy Economics,2002,24(1):55-70.