基于电力大数据的用户用电行为分析研究综述
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摘要
随着能源互联网与智能电网技术的不断发展,电力大数据蕴含的潜在价值也在不断被挖掘。以电力大数据为基础,介绍了电力大数据技术和国内外关于电力大数据分析用户用电行为的实例,最后介绍了应用电力大数据对用户进行分类,确定用户用电行为影响因子和对用户用电行为进行分析的常用研究方法。
With continuous development of the energy Internet and smart grid technology,the potential value hidden in the big power data has been mined constantly. Based on big power data,this paper introduced the big power data technology as well as domestic and foreign examples of power consumption behavior analysis. Finally,it introduced the ways in which big power data was used to classify users,to determine impact factors of power consumption behavior,and to analyze user's utilization behavior.
With continuous development of the energy Internet and smart grid technology,the potential value hidden in the big power data has been mined constantly. Based on big power data,this paper introduced the big power data technology as well as domestic and foreign examples of power consumption behavior analysis. Finally,it introduced the ways in which big power data was used to classify users,to determine impact factors of power consumption behavior,and to analyze user's utilization behavior.
引文
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[14]张素香,刘建明,赵丙镇,等.基于云计算的居民用电行为分析模型研究[J].电网技术,2013,37(6):1542-1546.
[15]王德文,宋亚奇,朱永利.基于云计算的智能电网信息平台[J].电力系统自动化,2010,34(22):7-12.
[16]麦鸿坤,肖坚红,吴熙辰,等.基于R语言的负荷预测ARIMA模型并行化研究[J].电网技术,2015,39(11):3216-3220.
[2]彭小圣,邓迪元,程时杰,等.面向智能电网应用的电力大数据关键技术[J].中国电机工程学报,2015,35(3):503-511.
[3] FAYYAD U,PIATETSKY-SHAPIRO G,SMYTH P. From data mining to knowledge discovery[M]. American:American Association for Artificeal Intelligence,1996.
[4]宋亚奇,周国亮,朱永利,等.智能电网大数据处理技术现状与挑战[J].电网技术,2013,37(4):927-935.
[5]沈玉玲,吕燕,陈瑞峰.基于大数据技术的电力用户行为分析及应用现状[J].电气自动化,2016,38(3):50-52.
[6] Electric Power Research Institute. Data analytics initiative for transmission and distribution:year three update[EB/OL].(2016).[2017-12-13]. https://www. epri. com/#/pages/product/3002007539/.
[7] WANG X W,WANG Y Z. Introduction of german smart grid “EEnergy”project promotion[J]. Power Demand Side Management,2011,330(10):670-674.
[8]张东霞,苗新,刘丽平,等.智能电网大数据技术发展研究[J].中国电机工程学报,2015,35(1):2-12.
[9]周湶,孙威,任海军,等.基于最小二乘支持向量机和负荷密度指标法的配电网空间负荷预测[J].电网技术,2011,35(1):66-71.
[10]杨旭英,周明,李庚银.智能电网下需求响应机理分析与建模综述[J].电网技术,2016,40(1):220-226.
[11]张欣,高卫国,苏运.基于函数型数据分析和k-means算法的电力用户分类[J].电网技术,2015,39(11):3153-3162.
[12]张少敏,赵硕,王保义.基于云计算和量子粒子群算法的电力负荷曲线聚类算法研究[J].电力系统保护与控制,2014,42(21):93-98.
[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.
[14]张素香,刘建明,赵丙镇,等.基于云计算的居民用电行为分析模型研究[J].电网技术,2013,37(6):1542-1546.
[15]王德文,宋亚奇,朱永利.基于云计算的智能电网信息平台[J].电力系统自动化,2010,34(22):7-12.
[16]麦鸿坤,肖坚红,吴熙辰,等.基于R语言的负荷预测ARIMA模型并行化研究[J].电网技术,2015,39(11):3216-3220.