北极星

搜索历史清空

  • 水处理
您的位置:电力配售电能源服务评论正文

深度|用户节电的大数据分析及应用

2019-04-15 10:43来源:电网技术关键词:电力用户电价售电收藏点赞

投稿

我要投稿

表A1 偏最小二乘回归结果Tab. A1 Partial least squares regression results

图A4 数据联动示意图Fig. A4 Diagram of data interaction

致 谢

北京邮电大学硕士研究生陈璞迪和宋礼在节电可视化开发中提供了极大帮助,在此表示由衷感谢。

附录见本刊网络版(http://www.dwjs.com.cn/CN/volumn/current.shtml)。

参考文献

[1]王雁凌,王晶晶,吴京锴,等.能效电厂项目的节电潜力优化模型[J].电网技术,2014,38(4):941-946.WangYanling,WangJingjing,WuJingkai,et al.An electricity-saving potential optimization model of efficiency power plant[J].Power System Technology,2014,38(4):941-946(in Chinese).

[2]冯天瑞,黄瑞艺,石怡理,等.电网企业建筑节电方法及其潜力分析[J].电力需求侧管理,2013,15(5):27-31.FengTianrui,HuangRuiyi,ShiYili,et al.Building power-saving method of power grid enterprises and its potential analysis[J].Power Demand Side Management,2013,15(5):27-31(in Chinese).

[3]Dubois MC,ÅkeBlomsterberg.Energy saving potential and strategies for electric lighting in future North European,low energy office buildings:A literature review[J].Energy & Buildings,2011,43(10):2572-2582.

[4]LiK,LinB.The improvement gap in energy intensity:Analysis of China's thirty provincial regions using the improved DEA(data envelopment analysis) model[J].Energy,2015(84):589-599.

[5]RaoX,WuJ,ZhangZ,et al.Energy efficiency and energy saving potential in China:An analysis based on slacks-based measure model[J].Computers & Industrial Engineering,2012,63(3):578-584.

[6]赵腾,张焰,张东霞.智能配电网大数据应用技术与前景分析[J].电网技术,2014,38(12):3305-3312.ZhaoTeng,ZhangYan,ZhangDongxia.Application technology of big data in smart distribution grid and its prospect analysis[J].Power System Technology,2014,38(12):3305-3312(in Chinese).

[7]赵岩,李磊,刘俊勇,等.上海电网需求侧负荷模式的组合识别模型[J].电网技术,2010,34(1):145-151.ZhaoYan,LiLei,LiuJunyong,et al.Combinational recognition model for demand side load profile in shanghai power grid[J].Power System Technology,2010,34(1):145-151(in Chinese).

[8]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.WangDewen,SunZhiwei.Big data analysis and parallel load foreing of electric power user side[J].Proceedings of the CSEE,2015,35(3):527-537(in Chinese).

[9]丁坚勇,周凯,田世明,等.基于大数据技术的重要用户供电安全分析[J].电网技术,2016,40(8):2491-2495.DingJianyong,ZhouKai,TianShiming,et al.Big data technology based important user power supply security analysis[J].Power System Technology,2016,40(8):2491-2495(in Chinese).

[10]王德文,周青.一种电力设备状态监测大数据的分布式联机分析处理方法[J].中国电机工程学报,2016,36(19):5111-5121.WangDewen,ZhouQing.A method of distributed on-line analytical processing of status monitoring big data of electric power equipment[J].Proceedings of the CSEE,2016,36(19):5111-5121(in Chinese).

[11]丁杰,奚后玮,韩海韵,等.面向智能电网的数据密集型云存储策略[J].电力系统自动化,2012,36(12):66-70.DingJie,XiHouwei,HanHaiyun,et al.A smart grid-oriented data placement strategy for data-intensive cloud environment[J].Automation of Electric Power Systems,2012,36(12):66-70(in Chinese).

[12]中国电机工程学会信息化专委会.中国电力大数据发展白皮书[S].北京:中国电力出版社,2013.

[13]赵莉,候兴哲,胡君,等.基于改进K-Means算法的海量智能用电数据分析[J].电网技术,2014,38(10):2715-2720.ZhaoLi,HouXingzhe,HuJun,et al.Improved K-Means algorithm based analysis on massive data of intelligent power utilization[J].Power System Technology,2014,38(10):2715-2720(in Chinese).

[14]朱文俊,王毅,罗敏,等.面向海量用户用电特性感知的分布式聚类算法[J].电力系统自动化,2016,40(12):21-27.ZhuWenjun,WangYi,LuoMin,et al.Distributed clustering algorithm for awareness of electricity consumption acteristics of massive consumers[J].Automation of Electric Power Systems,2016,40(12):21-27(in Chinese).

[15]肖琪.基于优化K-means算法的电力负荷分类研究[D].大连:大连理工大学,2015.

[16]周凯.大数据背景下线损和用电特性分析研究[D].天津:天津大学,2016.

[17]赵腾,王林童,张焰,等.采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J].中国电机工程学报,2016,36(3):604-614.ZhaoTeng,WangLintong,ZhangYan,et al.Relation factor identification of electricity consumption behavior of users and electricity demand foreing based on mutual information and random forests[J].Proceedings of the CSEE,2016,36(3):604-614(in Chinese).

[18]PérezgonzálezA,VergaraM,Sanchobru JL,et al.Visualizing data using t-SNE[J].Journal of Machine Learning Research,2017,9(2605):2579-2605.

[19]李蕊,李跃,徐浩,等.基于层次分析法和专家经验的重要电力用户典型供电模式评估[J].电网技术,2014,38(9):2336-2341.LiRui,LiYue,XuHao,et al.Assessment on typical power supply mode for important power consumers based on analytical hierarchy process and expert experience[J].Power System Technology,2014,38(9):2336-2341(in Chinese).

[20]罗耀明,毛李帆,姚建刚,等.电力用户综合能效评估模型[J].电力系统及其自动化学报,2011,23(5):104-109.LuoYaoming,MaoLifan,YaoJiangang,et al.Evaluation model of integrated energy efficiency for power users[J].Proceedings of the CSU-EPSA,2011,23(5):104-109(in Chinese).

[21]毛李帆,江岳春,龙瑞华,等.基于偏最小二乘回归分析的中长期电力负荷预测[J].电网技术,2008,32(19):71-77.MaoLifan,JiangYuechun,LongRuihua,et al.Medium- and long- term load foreing based on partial least squares regression analysis[J].Power System Technology,2008,32(19):71-77(in Chinese).

[22]VenkataramanS,GhodsiA,GhodsiA,et al.SparkR:scaling R programs with spark[C]//International Conference on Management of Data.ACM,2016:1099-1104.

[23]MengX,BradleyJ,YavuzB,et al.MLlib:machine learning in apache spark[J].Journal of Machine Learning Research,2016,17(1):1235-1241.

[24]沈国辉,佘东香,孙湃,等.电力系统可视化技术研究及应用[J].电网技术,2009,33(17):31-36.ShenGuohui,SheDongxiang,SunPai,et al.Research and application of power system visualization technology[J].Power System Technology,2009,33(17):31-36(in Chinese).

[25]杨德尚,袁荣湘,李启旺,等.可视化黑启动决策支持软件开发研究[J].电力系统保护与控制,2010,38(5):97-101.YangDeshang,YuanRongxiang,LiQiwang,et al.Visual black startup decision support software development research[J].Power System Protection and Control,2010,38(5):97-101(in Chinese).

[26]赖晓文,陈启鑫,夏清,等.基于SVG技术的电力系统可视化平台集成与方法库开发[J].电力系统自动化,2012,36(16):76-81.LaiXiaowen,ChenQixin,XiaQing,et al.Development of integrated and method base for power system visualization platform based on SVG technology[J].Automation of Electric Power Systems,2012,36(16):76-81(in Chinese).

原标题:用户节电的大数据分析及应用
投稿与新闻线索:陈女士 微信/手机:13693626116 邮箱:chenchen#bjxmail.com(请将#改成@)

特别声明:北极星转载其他网站内容,出于传递更多信息而非盈利之目的,同时并不代表赞成其观点或证实其描述,内容仅供参考。版权归原作者所有,若有侵权,请联系我们删除。

凡来源注明北极星*网的内容为北极星原创,转载需获授权。