Pattern Analysis of Regional Energy Consumption Based on Integrated Energy Measurement Data

doi:10.11930/j.issn.1004-9649.202105059

Abstract

Abstract: The spatio-temporal pattern of users’ energy consumption can offer an important guideline for power system planning, and it is also one of the most important basis for power grid construction. Through widely deployed new-type advanced metering infrastructures, power grid operators are able to analyze the users’ energy consumption patterns, which can promote the efficient operation of power grids. Towards regional user profile for energy consumption, a regional energy consumption pattern analysis approach is proposed based on integrated energy measurement data. Using multi-meter integration energy-consumption acquisition system, the gas consumption data is introduced for analysis. The hierarchical clustering and self-organized maps (SOM) methods are used to describe the energy consumption patterns. Simulation and experimental results show that the proposed method is of practical use in energy consumption analysis and conducive to discovering high energy-consumption regions, which is helpful for power grid operators to formulate power system planning schemes.

Key words: energy consumption pattern, pattern analysis, multi-meter integration, clustering algorithm

WANG Xingang, ZHAO Fang, ZHU Wenjun. Pattern Analysis of Regional Energy Consumption Based on Integrated Energy Measurement Data[J]. Electric Power, 2022, 55(9): 140-145.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202105059

https://www.electricpower.com.cn/EN/Y2022/V55/I9/140

References

[1] 郑楷洪, 杨劲锋, 王鑫, 等. 用电量数据的可视化研究综述[J]. 电力系统保护与控制, 2022, 50(9): 179–187
ZHENG Kaihong, YANG Jinfeng, WANG Xin, et al. Overview of visualization research on electricity consumption data[J]. Power System Protection and Control, 2022, 50(9): 179–187
[2] 方紫筠, 钱玉良. 含有用户自主选择行为分析的电价套餐设计策略[J]. 智慧电力, 2022, 50(5): 94–99,105
FANG Ziyun, QIAN Yuliang. Design strategy of electricity price package with analysis of user's independent choice behavior[J]. Smart Power, 2022, 50(5): 94–99,105
[3] 白东壮, 田世明, 邹毅豪, 等. 基于FDA的居民用户空调用电行为分类分析方法[J]. 智慧电力, 2022, 50(3): 44–49, 71
BAI Dongzhuang, TIAN Shiming, ZOU Yihao, et al. Classification analysis method of residential air conditioning electricity consumption behavior based on functional data analysis model[J]. Smart Power, 2022, 50(3): 44–49, 71
[4] 李强, 宋宁希, 王剑晓, 等. 基于用户互动能力的优化用电模式与方法[J]. 电网技术, 2016, 40(6): 1818–1824
LI Qiang, SONG Ningxi, WANG Jianxiao, et al. A pattern and method of optimized power utilization based on consumers' interaction capability[J]. Power System Technology, 2016, 40(6): 1818–1824
[5] 张立辉, 戴谷禹, 聂青云, 等. 碳交易机制下计及用电行为的虚拟电厂经济调度模型[J]. 电力系统保护与控制, 2020, 48(24): 154–163
ZHANG Lihui, DAI Guyu, NIE Qingyun, et al. Economic dispatch model of virtual power plant considering electricity consumption under a carbon trading mechanism[J]. Power System Protection and Control, 2020, 48(24): 154–163
[6] 徐晴, 刘建, 田正其, 等. 水、热、气、电四表合一数据采集系统的研究与应用[J]. 计算机测量与控制, 2017, 25(3): 217–221
XU Qing, LIU Jian, TIAN Zhengqi, et al. Research and application on data acquisition system about water, heat, gas, and electricity meters in one[J]. Computer Measurement & Control, 2017, 25(3): 217–221
[7] LIU G Y, YANG J, HAO Y, et al. Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering[J]. Journal of Cleaner Production, 2018, 183: 304–314.
[8] 孙毅, 冯云, 崔灿, 等. 基于动态自适应K均值聚类的电力用户负荷编码与行为分析[J]. 电力科学与技术学报, 2017, 32(3): 3–8
SUN Yi, FENG Yun, CUI Can, et al. Power user load code and behavior analysis based on dynamic adaptive k-means clustering[J]. Journal of Electric Power Science and Technology, 2017, 32(3): 3–8
[9] 孙义豪, 王璟, 郭勇, 等. 一种基于配电网评价指标体系和聚类分析的电网区域分类方法[J]. 武汉大学学报(工学版), 2018, 51(9): 817–822,846
SUN Yihao, WANG Jing, GUO Yong, et al. Area classification based on distribution network evaluation index system and cluster analysis[J]. Engineering Journal of Wuhan University, 2018, 51(9): 817–822,846
[10] 董志辉, 林凌雪. 基于改进模糊C均值聚类时段划分的配电网动态重构[J]. 电网技术, 2019, 43(7): 2299–2305
DONG Zhihui, LIN Lingxue. Dynamic reconfiguration of distribution network based on improved fuzzy C-means clustering of time division[J]. Power System Technology, 2019, 43(7): 2299–2305
[11] 石亮缘, 周任军, 张武军, 等. 采用深度学习和多维模糊C均值聚类的负荷分类方法[J]. 电力系统及其自动化学报, 2019, 31(7): 43–50
SHI Liangyuan, ZHOU Renjun, ZHANG Wujun, et al. Load classification method using deep learning and multi-dimensional fuzzy C-means clustering[J]. Proceedings of the CSU-EPSA, 2019, 31(7): 43–50
[12] CHENG X D, SCHERPEN J M A. Clustering approach to model order reduction of power networks with distributed controllers[J]. Advances in Computational Mathematics, 2018, 44(6): 1917–1939.
[13] 赵凯, 侯玉强. 基于自组织映射神经网络K-means聚类算法的风电场多机等值建模[J]. 浙江电力, 2019, 38(8): 30–36
ZHAO Kai, HOU Yuqiang. Multi-machine equivalent modeling of wind farms using SOM-based K-means clustering[J]. Zhejiang Electric Power, 2019, 38(8): 30–36
[14] 谭宇航, 张朕滔, 袁玲, 等. 基于计量一体化的供电设备故障在线识别[J]. 控制工程, 2019, 26(6): 1133–1137
TAN Yuhang, ZHANG Zhentao, YUAN Ling, et al. Online failure identification method for power equipment based on metering automation and integration platform[J]. Control Engineering of China, 2019, 26(6): 1133–1137
[15] 张小斐, 魏玲, 王自强, 等. 基于负荷特性的大用户业扩辅助分析方法[J]. 电测与仪表, 2019, 56(12): 44–48,84
ZHANG Xiaofei, WEI Ling, WANG Ziqiang, et al. Auxiliary analysis method of power supply access for large consumers based on load characteristics[J]. Electrical Measurement & Instrumentation, 2019, 56(12): 44–48,84
[16] 潘明明, 田世明, 魏娜, 等. 基于数据划分的工业电力负荷曲线聚类研究[J]. 电气自动化, 2019, 41(4): 24–26,67
PAN Mingming, TIAN Shiming, WEI Na, et al. Research on industrial power load curve clustering based on data division[J]. Electrical Automation, 2019, 41(4): 24–26,67
[17] 钱潮恺, 黄德才. 基于维度频率相异度和强连通融合的混合数据聚类算法[J]. 模式识别与人工智能, 2016, 29(1): 82–89
QIAN Chaokai, HUANG Decai. Clustering algorithm for mixed data based on dimensional frequency dissimilarity and strongly connected fusion[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(1): 82–89
[18] 黄苑华. 分类型数据的聚类算法研究[D]. 广州: 广东工业大学, 2016.
HUANG Yuanhua. Studies on clustering algorithms for categorical data[D]. Guangzhou: Guangdong University of Technology, 2016.
[19] BARAI (DEB) A, DEY L. Outlier detection and removal algorithm in K-means and hierarchical clustering[J]. World Journal of Computer Application and Technology, 2017, 5(2): 24–29.
[20] 原野, 田园. 基于DTW层次聚类算法的电力负荷数据特征研究[J]. 自动化仪表, 2020, 41(12): 96–101
YUAN Ye, TIAN Yuan. Research on characteristics of power load data based on DTW hierarchical clustering algorithm[J]. Process Automation Instrumentation, 2020, 41(12): 96–101
[21] 徐杰彦, 许雯旸, 褚渊, 等. 区域尺度住宅建筑日用电负荷模型构建方法研究[J]. 中国电力, 2020, 53(8): 29–39
XU Jieyan, XU Wenyang, CHU Yuan, et al. Residential electricity load model construction in district scale[J]. Electric Power, 2020, 53(8): 29–39
[22] 苏欣, 田浩, 秦昌龙, 等. 多变量数据聚类最优选择的用电关联分析算法[J]. 电网与清洁能源, 2022, 38(4): 86-94, 103.
SU Xin, TIAN Hao, QIN Changlong, et al. Electricity consumption association analysis algorithm for optimal selection of multivariate data clustering[J]. Advances of Power System and Hydroelectric Engineering, 2022, 38(4): 86-94, 103.
[23] 杜勉, 易俊, 郭剑波, 等. 神经网络技术在风电机组SCADA数据分析中的应用研究[J]. 电网技术, 2018, 42(7): 2200–2205
DU Mian, YI Jun, GUO Jianbo, et al. Research on the application of neural networks on wind turbine SCADA data analysis[J]. Power System Technology, 2018, 42(7): 2200–2205
[24] 冯伟夏, 孟安波, 何双伯. 基于自组织网络的智能电网配电方案设计[J]. 现代电子技术, 2019, 42(9): 158–162
FENG Weixia, MENG Anbo, HE Shuangbo. Design of smart grid power distribution scheme based on self-organizing network[J]. Modern Electronics Technique, 2019, 42(9): 158–162
[25] 黄新波, 王宁, 朱永灿, 等. 基于RST-SOM的高压断路器故障诊断[J]. 高压电器, 2020, 56(3): 1–8
HUANG Xinbo, WANG Ning, ZHU Yongcan, et al. Fault diagnosis of high-voltage circuit breaker based on RST-SOM[J]. High Voltage Apparatus, 2020, 56(3): 1–8
[26] 黄红涛, 徐婷. 基于SOM聚类的用户信息数据自动挖掘算法研究[J]. 自动化与仪器仪表, 2022(7): 26–30
HUANG Hongtao, XU Ting. Research on automatic mining algorithm of user information data based on SOM clustering[J]. Automation & Instrumentation, 2022(7): 26–30
[27] 王杰, 丁明, 孙磊, 等. 基于改进聚类算法的关键输电断面搜索方法[J]. 中国电力, 2022, 55(6): 86–94
WANG Jie, DING Ming, SUN Lei, et al. Key transmission section search strategy based on improved clustering algorithm[J]. Electric Power, 2022, 55(6): 86–94
[28] 杨建华, 肖达强, 张伟, 等. 基于改进RBFNN的1 000 kV特高压线损预测[J]. 中国电力, 2022, 55(5): 122–127, 142
YANG Jianhua, XIAO Daqiang, ZHANG Wei, et al. Prediction of 1 000 kV UHV line loss based on improved RBFNN[J]. Electric Power, 2022, 55(5): 122–127, 142
[29] 陈卫东, 郭敏, 吴宁, 等. 基于图像匹配的微电网负荷响应分布式电源波动控制方法[J]. 中国电力, 2022, 55(3): 57–63, 73
CHEN Weidong, GUO Min, WU Ning, et al. The control method that load respond to distributed generation fluctuation in microgrid based on image matching[J]. Electric Power, 2022, 55(3): 57–63, 73
[30] 马宗彪, 许素安, 朱少斌, 等. 基于特征加权模糊聚类的电力负荷分类[J]. 中国电力, 2022, 55(6): 25–32
MA Zongbiao, XU Suan, ZHU Shaobin, et al. Power load classification based on feature weighted fuzzy clustering[J]. Electric Power, 2022, 55(6): 25–32
[31] 苏剑涛, 郑书婷, 严干贵, 等. 基于改进FCM聚类算法的风电场等值建模研究[J]. 智慧电力, 2021, 49(10): 68–74
SU Jiantao, ZHENG Shuting, YAN Gangui, et al. Equivalent modeling of wind farm based on improved FCM clustering algorithm[J]. Smart Power, 2021, 49(10): 68–74