任勇;曾鸣;

• 1:华北电力大学经济与管理学院
• 2:贵州电网有限责任公司

摘要(Abstract)：

针对电力大用户的精准负荷预测对于配电网发展规划、调控运行、安全可靠供电具有重要意义,电力负荷预测是泛在电力物联网中实现用户精准用电感知的基础工作。为了在负荷预测模型中引入用户用电特征,提出了簇负荷特性曲线的概念,进而提出了一种基于簇负荷特性曲线的"聚类-回归"电力大用户短期负荷预测方法。首先,对区域内电力大用户用电特征进行聚类分析,用电特征类似的用户聚为一簇,用簇负荷特性曲线表征簇内用户用电特征;其次,回归阶段将簇负荷特性曲线作为区域总负荷的属性因子,训练深度神经网络,进行负荷预测。在Tensor Flow深度学习框架下实现了"聚类-回归"负荷预测模型,以我国西南某区域电网实际数据设计实验,验证了模型的准确性;为了达到最佳预测效果,对模型超参数进行了优化;进一步考虑到电力大数据的应用环境,设计了压力测试,验证了模型的有效性。方法可以良好应用于电力大数据环境下的大用户负荷预测。

关键词(KeyWords)： 短期电力负荷预测;簇负荷特性曲线;电力大用户;“聚类-回归”模型

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(61472236)

作者(Author): 任勇;曾鸣;

Email:

DOI:

参考文献(References)：

• [1]国务院．电力体制改革方案(国发[2002]5号)[Z]．北京:国务院,2002．
• [2]国家电力监管委员会,国家发展和改革委员会．电力用户向发电企业直接购电试点暂行办法(电监输电[2004]17号)[Z]．北京:国家电力监管委员会,国家发展和改革委员会,2004．
• [3]国务院．国务院办公厅转发电力体制改革工作小组关于十一五深化电力体制改革的实施意见的通知(国办发[2007]19号)[Z]．北京:国务院,2007．
• [4]关于推进“互联网+”智慧能源发展的指导意见[EB/OL].(2016-02-29)．
• [5]国家电网《泛在电力物联网建设大纲》正式发布[EB/OL].(2019-03-11)．
• [6]刘羽霄,张宁,康重庆．数据驱动的电力网络分析与优化研究综述[J]．电力系统自动化,2018,42(6):157-167.LIU Yuxiao,ZHANG Ning,KANG Chongqing.A review on datadriven analysis and optimization of power grid[J].Automation of Electric Power Systems,2018,42(6):157-167．
• [7]KANG Chongqing,WANG Yi,XUE Yusheng,et al.Big data analytics in China’s electric power industry:modern information,communication technologies,and millions of smart meters[J].IEEE Power and Energy Magazine,2018,16(3):54-65．
• [8]王毅,陈启鑫,张宁,等．5G通信与泛在电力物联网的融合:应用分析与研究展望[J]．电网技术,2019,43(5):1575-1585.WANG Yi,CHEN Qixin,ZHANG Ning,et al.Fusion of the 5G communication and the ubiquitous electric internet of things:application analysis and research prospects[J].Power System Technology,2019,43(5):1575-1585．
• [9]傅质馨,李潇逸,袁越．泛在电力物联网关键技术探讨[J]．电力建设,2019,40(5):1-12.FU Zhixin,LI Xiaoyi,YUAN Yue.Research on key technologies of Ubiquitous Power Internet of Things[J].Electric Power Construction,2019,40(5):1-12．
• [10]KHANI H,VARMA R K,ZADEH M R D,et al.AReal-time multistep optimization-based model for scheduling of storage-based large-scale electricity consumers in a wholesale market[J].IEEE Transactions on Sustainable Energy,2017,8(2):836-845．
• [11]KAZEMPOUR S J,CONEJO A J,RUIZ C.Strategic bidding for a large consumer[J].IEEE Transactions on Power Systems,2015,30(2):848-856．
• [12]ZARIF M,JAVIDI M H,GHAZIZADEH M S.Selfscheduling of large consumers with second-order stochastic dominance constraints[J].IEEE Transactions on Power Systems,2013,28(1):289-299．
• [13]罗敏,程将南,王毅,等．基于小波去噪和决策树的个性化大用户负荷预测[J]．南方电网技术,2016,10(10):37-42.LUO Min,CHENG Jiangnan,WANG Yi,et al.Wavelet de-noising and decision tree based load forecasting of large consumers[J].Southern Power System Technology,2016,10(10):37-42．
• [14]肖泽青,华昊辰,曹军威．人工智能在能源互联网中的应用综述[J]．电力建设,2019,40(5):63-70.XIAO Zeqing,HUA Haochen,CAO Junwei.Overview of the application of artificial intelligence in energy internet[J].Electric Power Construction,2019,40(5):63-70．
• [15]欧阳旭,朱向前,叶伦,等．区块链技术在大用户直购电中的应用初探[J]．中国电机工程学报,2017,37(13):3737-3745.OUYANG Xu,ZHU Xiangqian,YE Lun,et al.Preliminary applications of blockchain technique in large consumers direct power trading[J].Proceedings of the CSEE,2017,37(13):3737-3745．
• [16]夏清,白杨,钟海旺,等．中国推广大用户直购电交易的制度设计与建议[J]．电力系统自动化,2013,37(20):1-7.XIA Qing,BAI Yang,ZHONG Haiwang,et al.Institutional design and suggestions for promotion of direct electricity purchase by large consumers in china[J].Automation of Electric Power Systems,2013,37(20):1-7．
• [17]李言,王秀丽,张文韬,等．大用户直购电对系统风电消纳的影响[J]．电工技术学报,2017,32(23):149-157.LI Yan,WANG Xiuli,ZHANG Wentao,et al.Impact of large consumers direct-purchasing on consumption of wind power of power systems[J].Transactions of China Electrotechnical Society,2017,32(23):149-157．
• [18]张文韬,王秀丽,吴雄,等．大规模风电接入下含大用户直购电的电力系统调度模型研究[J]．中国电机工程学报,2015,35(12):2927-2935.ZHANG Wentao,WANG Xiuli,WU Xiong,et al.An analysis model of power system with large-scale wind power and transaction mode of direct power purchase by large consumers involved in system scheduling[J].Proceedings of the CSEE,2015,35(12):2927-2935．
• [19]申建建,曹瑞,苏承国,等．水火风光多源发电调度系统大数据平台架构及关键技术[J]．中国电机工程学报,2019,39(1):43-55.SHEN Jianjian,CAO Rui,SU Chengguo,et al.Big data platform architecture and key techniques of power generation scheduling for hydro-thermal-wind-solar hybrid system[J].Proceedings of the CSEE,2019,39(1):43-55．
• [20]张素香,赵丙镇,王风雨,等．海量数据下的电力负荷短期预测[J]．中国电机工程学报,2015,35(1):37-42.ZHANG Suxiang,ZHAO Bingzhen,WANG Fengyu,et al.Short-term power load forecasting based on big data[J].Proceedings of the CSEE,2015,35(1):37-42．
• [21]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．
• [22]LI M,ZHOU Q.Distribution transformer mid-term heavy load and overload pre-warning based on logistic regression[C].2015 IEEE Eindhoven PowerTech,Eindhoven,2015．
• [23]雷景生,郝珈玮,朱国康．基于“分层-汇集”模型的短期电力负荷预测[J]．电力建设,2017,38(1):68-75.LEI Jingsheng,HAO Jiawei,ZHU Guokang.Short-term power load forecasting based on layered-confluence model[J].Electric Power Construction,2017,38(1):68-75．
• [24]马天男,牛东晓,黄雅莉,等．基于Spark平台和多变量L_2-Boosting回归模型的分布式能源系统短期负荷预测[J]．电网技术,2016,40(6):1642-1649.MA Tiannan,NIU Xiaodong,HUANG Yali.Shortterm load forecasting for distributed energy system based on spark platform and multi-variable L2-boosting regression model[J].Power System Technology,2016,40(6):1642-1649．
• [25]刘琪琛,雷景生,郝珈玮,等．基于Spark平台和并行随机森林回归算法的短期电力负荷预测[J]．电力建设,2017,38(10):84-92.LIU Qichen,LEI Jingsheng,HAO Jiawei,et al.Shortterm power load forecasting based on spark platform and parallel random forest regression algorithm model[J].Electric Power Construction,2017,38(10):84-92．
• [26]王德文,孙志伟．电力用户侧大数据分析与并行负荷预测[J]．中国电机工程学报,2015,35(3):527-537.WANG Dewen,SUN Zhiwei.Big data analysis and parallel load forecasting of electric power user side[J].Proceedings of the CSEE,2015,35(3):527-537．
• [27]张昭昭,乔俊飞,余文．多层自适应模块化神经网络结构设计[J]．计算机学报,2017,40(12):2827-2838.ZHANG Zhao Zhao,QIAO Junfei,YU Wen.Structure design of hierarchical adaptive modular neural network[J].Chinese Journal of Computers,2017,40(12):2827-2838．
• [28]黄立威,江碧涛,吕守业,等．基于深度学习的推荐系统研究综述[J]．计算机学报,2018,41(7):1619-1647.HUANG Liwei,JIANG Bitao,LV Shouye,et al.Survey on deep learning based recommender systems[J].Chinese Journal of Computers 2018,41(7):1619-1647．
• [29]易灵芝,常峰铭,龙谷宗,等．基于进化深度学习短期负荷预测的应用研究[J]．电力系统及其自动化学报,2020,32(3):1-6.YI Lingzhi,CHANG Fengming,LONG Guzong,et al.Short-term load forecasting method based on evolutionary deep learning[J].Proceedings of the CSU-EPSA,2020,32(3):1-6．
• [30]张宇帆,艾芊,林琳,等．基于深度长短时记忆网络的区域级超短期负荷预测方法[J]．电网技术,2019,43(6):1884-1892.ZHANG Yufan,AI Qian,LIN Lin,et al.A very shortterm load forecasting method based on deep LSTM RNNat zone level[J].Power System Technology,2019,43(6):1884-1892．
• [31]杨智宇,刘俊勇,刘友波,等．基于自适应深度信念网络的变电站负荷预测[J]．中国电机工程学报,2019,39(14):4049-4061.YANG Zhiyu,LIU Junyong,LIU Youbo,et al.Transformer load forecasting based on adaptive deep belief network[J].Proceedings of the CSEE,2019,39(14):4049-4061．
• [32]张倩,马愿,李国丽,等．频域分解和深度学习算法在短期负荷及光伏功率预测中的应用[J]．中国电机工程学报,2019,39(8):2221-2230.ZHANG Qian,MA Yuan,LI Guoli,et al.Applications of frequency domain decomposition and deep learning algorithms in short-term load and photovoltaic power forecasting[J].Proceedings of the CSEE,2019,39(8):2221-2230．
• [33]MA M,LU J,TRYGGVASON G.Using statistical learning to close two-fluid multiphase flow equations for bubbly flows in vertical channels[J].International Journal of Multiphase Flow,2016,85:336-347.