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首页 > 过刊浏览>2022年第37卷第6期 >165-172. DOI:10.19781/j.issn.1673-9140.2022.06.019
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基于格兰杰因果与ARDL模型的高能耗产业用电量预测
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TM621

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福建省科学技术协会科技创新智库课题研究项目(Fjkx-B2009)； 国家重点研发计划(2018YFB0905200)


Research on forecasting electricity consumption of high-energy-consuming industries based on Granger causality and ARDL model
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    摘要:

    为挖掘产业经济发展与电力大数据之间的耦合关系,构建高耗能产业用电量与多元经济指标数据之间的向量自回归(VAR)模型。通过使用格兰杰(Granger)因果关系检验的方法,提取对用电量预测有显著影响的产业经济指标数据,在此基础上建立考虑经济因素影响的高耗能产业用电量自回归分布滞后(ARDL)模型。以某地区2016—2020年产业用电和经济数据进行算例分析,结果表明:Granger因果关系检验可以有效地挖掘与细分产业用电量相关联的经济指标;将这些经济因素考虑到产业用电预测模型中,可以有效地提高模型的预测精度。

    Abstract:

    In order to explore the coupling relationship between the industrial economic development and power big data, a vector auto regressive (VAR) model is constructed between the electricity consumption of high-energy-consuming industries and the data of multiple economic indicators. By using the Granger causality test method, industrial economic index data are extracted that has a significant impact on electricity consumption forecasting. Based on this, an Auto Regressive Distributed Lag (ARDL) model of electricity consumption is established in high-energy-consuming industries that takes economic factors into account. An example analysis of industrial electricity consumption and economic data in a certain region from 2016 to 2020 shows that the Granger causality test can effectively dig out the economic indicators related to the electricity consumption of subdivided industries. Considering these economic factors in the regression model, the prediction accuracy of the model is effectively improved.

    参考文献
    [1] 李泽文,邓拓夫,曾祥君,等.智能电网能量流的时空多尺度大数据探讨[J].电力科学与技术学报,2015,30(1):22-27.LI Zewen,DENG Tuofu,ZENG Xiangjun,et al.Study on big data with time-space multi-scale for smart grid power flow[J].Journal of Electric Power Science and Technology,2015,30(1):22-27.
    [2] 莫文火,陈碧云.基于邻域关系矩阵的电力大数据增量式属性约简研究[J].高压电器,2020,56(8):192-197+204.MO Wenhuo,CHEN Biyun.Incremental attribute reduction of electric power big data based on neighborhood relation matrix[J].High Voltage Apparatus,2020,56(8):192-197+204.
    [3] 韩肖清,李廷钧,张东霞,等.双碳目标下的新型电力系统规划新问题及关键技术[J].高电压技术,2021,47(9):3036-3046.HAN Xiaoqing,LI Tingjun,ZHANG Dongxia,et al.New issues and key technologies of low-carbon power system planning under double carbon goals[J].High Voltage Engineering,2021,47(9):3036-3046.
    [4] 谭显东,刘俊,徐志成,等.“双碳”目标下“十四五”电力供需形势[J].中国电力,2021,54(5):1-6.TAN Xiandong,LIU Jun,XU Zhicheng,et al.Power supply and demand situation in the 14th Five-Year Plan under the "Double Carbon" target[J].Electric Power,2021,54(5):1-6.
    [5] 童光毅.基于双碳目标的智慧能源体系构建[J].智慧电力,2021,49(5):1-6.TONG Guangyi.The construction of a smart energy system based on dual carbon goals[J].Smart Power,2021,49(5):1-6.
    [6] ZHANG C,ZHOU K L,YANG S L,et al.On electricity consumption and economic growth in China[J].Renew Sustain Energy Reviews,2017,76:353-368.
    [7] LIN B Q,OMOJU O E,OKONKWo J U.Factors influencing renewable electricity consumption in China[J].Renew Sustain Energy Reviews,2016,55:687-696.
    [8] GE F,YE B,XING S N,et al.The analysis of the underlying reasons of the inconsistent relationship between economic growth and the consumption of electricity in China-a case study of Anhui province[J].Energy,2017,128:601-608.
    [9] LIU D,RUAN L,LIU J C,et al.Electricity consumption and economic growth nexus in Beijing:a causal analysis of quarterly sectoral data[J].Renewable Sustainable Energy Reviews,2018,82:2498-2503.
    [10] LI K,YUAN W H.The nexus between industrial growth and electricity consumption in China-new evidence from a quantile-on-quantile approach[J].Energy,2021,231:120991.
    [11] 庞传军,张波,余建明,等.基于结构化负荷模型的电力负荷概率区间预测[J].中国电力,2021,54(9):89-95.PANG Chuanjun,ZHANG Bo,YU Jianming,et al.Probabilistic interval forecasting of power load based on structured load model[J].Electric Power,2021,54(9):89-95.
    [12] 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.
    [13] 李强,赵健,王磊,等.配电网馈线负荷预测及风险评估预警分析方法[J].供用电,2020,37(5):50-55.LI Qiang,ZHAO Jian,WANG Lei,et al.Distribution network feeder load forecasting and risk assessment warning analysis method[J].Distribution & Utilization,2020,37(5):50-55.
    [14] 王雁凌,吴梦凯.经济新常态下基于偏最小二乘回归的中长期负荷预测模型[J].电力自动化设备,2018,38(3):133-139.WANG Yanling,WU Mengkai.Medium and long-term load forecasting model based on partial least squares regression under the new economic normal[J].Electric Power Automation Equipment,2018,38(3):133-139.
    [15] 张瑶,王傲寒,张宏.中国智能电网发展综述[J].电力系统保护与控制,2021,49(5):180-187.ZHANG Yao,WANG Aohan,ZHANG Hong.Overview of smart grid development in China[J].Power System Protection and Control,2021,49(5):180-187.
    [16] 刘江永,刘文翰,易灵芝.多时序协同中期负荷预测模型[J].电力系统及其自动化学报,2020,32(2):48-53.LIU Jiangyong,LIU Wenhan,YI Lingzhi.Multi-sequence coordinated medium-term load forecasting model[J].Proceedings of the CSU-EPSA,2020,32(2):48-53.
    [17] 王飞,李正辉,李渝,等.基于数据序列分辨率压缩尺度优化的月度电量预测方法[J].电力系统保护与控制,2020,48(11):62-68.WANG Fei,LI Zhenghui,LI Yu,et al.Data series resolution compression scale optimization based monthly electricity consumption forecasting[J].Power System Protection and Control,2020,48(11):62-68.
    [18] 陈浩文,刘文霞,李月乔.基于奇异谱分析与神经网络的中期负荷预测[J].电网技术,2020,44(4):1333-1347.CHEN Haowen,LIU Wenxia,LI Yueqiao.Medium-term load forecast based on singular spectrum analysis and neural network[J].Power System Technology,2020,44(4):1333-1347.
    [19] 王永伟,李新龙,田斐,等.基于人群搜索算法的电网短期用电负荷预测研究[J].电网与清洁能源,2020,36(12):35-40.WANG Yongwei,LI Xinlong,TIAN Fei,et al.Research on short-term electric load forecasting of power grid based on crowd search algorithm[J].Power System and Clean Energy,2020,36(12):35-40.
    [20] CHAREMZA W W,DEADMAN D F.New directions in econometric analysis[M].Oxford:Oxford University Press,1997:150-153.
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沈豫,黄夏楠,刘林,等.基于格兰杰因果与ARDL模型的高能耗产业用电量预测[J].电力科学与技术学报,2022,37(6):165-172.
SHEN Yu, HUANG Xianan, LIU Lin, et al. Research on forecasting electricity consumption of high-energy-consuming industries based on Granger causality and ARDL model[J]. Journal of Electric Power Science and Technology,2022,37(6):165-172.

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