期刊文献+

基于大数据分析的陕西省居民用电行为及影响因素研究 被引量:9

Research on Electricity Consumption Behavior and Influencing Factors of Residents in Shaanxi Province Based on Big Data Analysis
下载PDF
导出
摘要 陕西省目前处于发展的关键时期,为了有效的了解陕西省居民用电模式,通过对陕西省居民用电量以及相关经济、社会指标等众多数据的统计计算,得到了2000年以来的社会经济以及用电大数据。主要从陕西省居民用电增长情况、居民用电占比、人均用电水平和城乡居民用电差异等方面做了详细的分析和研究,对全面了解及把控陕西省居民用电行为具有十分重要的意义。 Shaanxi Province is currently in a critical period of development.In order to effectively understand the electricity consumption mode of residents in the province,based on the statistical calculation of the residential electricity consumption and related economic and social indicators in the province,this paper has obtained the big data of social economy and electricity consumption since 2000.Furthermore,the paper has made a detailed analysis and research mainly on the growth of residential electricity consumption,the proportion of residential electricity consumption,the per capita electricity consumption level and the electricity consumption difference between urban and rural residents,which is of great significance to comprehensively understand and control the residential electricity consumption behavior in Shaanxi Province.
作者 李永毅 石蓉 郎锐 王开艳 贾嵘 LI Yongyi;SHI Rong;LANG Rui;WANG Kaiyan;JIA Rong(State Grid Shaanxi Electric Power Company,Xian 710048,Shaanxi,China;State Grid Shaanxi Electric Power Research Institute,Xi’an 710054,Shaanxi,China;Xi’an University of Technology,Xi’an 710048,Shaanxi,China)
出处 《电网与清洁能源》 2019年第4期43-48,共6页 Power System and Clean Energy
基金 国家电网重点课题“居民用电行为及潜力研究”(2018610002000497) 陕西省科技厅重点研发计划项目(2018ZDXM-GY-169) 西安市科技局“智慧能源重点实验室”科技创新平台建设项目(201805057ZD8CG41)~~
关键词 用电行为 陕西省居民用电 用电模式 大数据 electricity use behavior electricity consumption of Shaanxi residents electricity mode big data
  • 相关文献

参考文献6

二级参考文献66

  • 1李天云,李想,刘辉军,王洪涛.基于谱聚类的电力负荷分类[J].吉林电力,2008,36(5):4-6. 被引量:2
  • 2冯丽,邱家驹.基于电力负荷模式分类的短期电力负荷预测[J].电网技术,2005,29(4):23-26. 被引量:33
  • 3王志勇,曹一家.电力客户负荷模式分析[J].电力系统及其自动化学报,2007,19(3):62-65. 被引量:15
  • 4贾慧敏,何光宇,方朝雄,李可文,姚宇臻,黄妹妹.用于负荷预测的层次聚类和双向夹逼结合的多层次聚类法[J].电网技术,2007,31(23):33-36. 被引量:26
  • 5Yang H T,Chen S C,Peng P C.Genetic k-means-algorithm-based classification of direct load-control curves[J].IEE ProceedingsGeneration,Transmission and Distribution,2005,152(4):489-495.
  • 6Nizar A H,Dong Z Y,Wang Y.Power utility nontechnical loss analysis with extreme learning machine method[J].IEEE Transactions on Power Systems,2008,23(3):946-955.
  • 7Chicco G,Napoli R,Postolache P,et al.Customer characterization options for improving the tariff offer[J].IEEE Transactions on Power Systems,2003,18(1):381-387.
  • 8Valero S,Ortiz M,Senabre C,et al.Methods for customer and demand response policies selection in new electricity markets[J].IET Generation,Transmission&Distribution,2007,1(1):104-110.
  • 9Carpaneto E,Chicco G,Napoli R,et al.Electricity customer classification using frequency-domain load pattern data[J].International Journal of Electrical Power&Energy Systems,2006,28(1):13-20.
  • 10Nagi J,Yap K S,Tiong S K.Nontechnical loss detection for metered customers in power utility using support vector machines[J].IEEE Transactions on Power Delivery,2010,25(2):1162-1171.

共引文献62

同被引文献123

引证文献9

二级引证文献41

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部