基于多任务联合模型的居民用电模式分类方法

doi:10.19595/j.cnki.1000-6753.tces.210763

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (9434 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract Identifying the electricity consumption behavior patterns of massive residential users and then making a reasonable classification, can provide auxiliary decision-making for demand-side lean management. This paper proposes a method of residential electricity consumption pattern classification based on a multi-task joint model of convolutional neural network auto-encoder(CNN-AE) and hierarchical clustering. Firstly, a method for filling missing values based on the mean value of simultaneous measurement data and an outlier detection method based on seasonal hybrid extreme studentized deviate test, were proposed to clean and correct massive and high-dimensional electricity data. Secondly, the CNN-AE was used to extract the features of the residential electricity consumption data, and obtained the feature vector which could characterize the residents'electricity consumption behavior. Then, combining the hierarchical clustering algorithm and silhouette coefficient to determine the number of users'cluster and each cluster centers'vector, initialized the neural network layer for user clustering with cluster centers'vector; and joined the feature extraction process and user clustering process to form a multi-task learning neural network. This network was used to achieve end-to-end classification of residential electricity consumption patterns. Finally, considering environmental temperature and electricity price factors, the proposed method was verified on actual dataset.

Key words： Residential load load clustering convolutional neural network auto-encoder joint model

Received: 27 May 2021

PACS:	TM769
	TP193

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xu Mingjie
	Zhao Jian
	Wang Xiaoyu
	Xuan Yi
	Chen Bojian

Cite this article:

Xu Mingjie,Zhao Jian,Wang Xiaoyu等. Residential Electricity Consumption Pattern Classification Method Based on Multi-Task Joint Model[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5490-5502.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210763 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I21/5490

[1] 雷怡琴, 孙兆龙, 叶志浩, 等. 电力系统负荷非侵入式监测方法研究[J]. 电工技术学报, 2021, 36(11): 2288-2297.
Lei Yiqin, Sun Zhaolong, Ye Zhihao, et al.Research on non-invasive load monitoring method in power system[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2288-2297.
[2] Wang Yi, Chen Qixin, Hong Tao, et al.Review of smart meter data analytics: applications, methodologies, and challenges[J]. IEEE Transactions on Smart Grid, 2019, 10(3): 3125-3148.
[3] 王孝慈, 董树锋, 王莉, 等. 基于电器状态关联分析的民可平移负荷辨识[J]. 电工技术学报, 2020, 35(23): 4961-4970.
Wang Xiaoci, Dong Shufeng, Wang Li, et al.Resident shiftable loads monitoring based on load states set correlation analysis[J]. Transactions of China Electrotechnical Society, 2020, 35(23): 4961-4970.
[4] 周东国, 张恒, 周洪, 等. 基于状态特征聚类的非侵入式负荷事件检测方法[J]. 电工技术学报, 2020, 35(21): 4565-4575.
Zhou Dongguo, Zhang Heng, Zhou Hong, et al.Non-intrusive load event detection method based on state feature clustering[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4565-4575.
[5] 涂青宇, 苗世洪, 张迪, 等. 分布式发电市场化环境下基于价格型需求响应的农村光伏交易模式研究[J]. 电工技术学报, 2020, 35(22): 4784-4797.
Tu Qingyu, Miao Shihong, Zhang Di, et al.Research on rural photovoltaic trading pattern based on price-based demand response under marketization environment of distributed generation[J]. Transactions of China Electrotechnical Society, 2020, 35(22): 4784-4797.
[6] Kong Weicong, Dong Zhaoyang, Jia Youwei, et al.Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 841-851.
[7] 王帅, 杜欣慧, 姚宏民, 等. 面向含多种用户类型的负荷曲线聚类研究[J]. 电网技术, 2018, 42(10): 3401-3412.
Wang Shuai, Du Xinhui, Yao Hongmin, et al.Research on load curve clustering with multiple user types[J]. Power System Technology, 2018, 42(10): 3401-3412.
[8] 王毅, 张宁, 康重庆, 等. 电力用户行为模型: 基本概念与研究框架[J]. 电工技术学报, 2019, 34(10): 2056-2068.
Wang Yi, Zhang Ning, Kang Chongqing, et al.Electrical consumer behavior model: basic concept and research framework[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 2056-2068.
[9] 徐磊, 杨秀, 张美霞. 基于数据挖掘的工业用户用电行为分析[J]. 电测与仪表, 2017, 54(16): 68-74.
Xu Lei, Yang Xiu, Zhang Meixia.Industrial users of electricity behavior analysis based on data mining[J]. Electrical Measurement ＆ Instrumentation, 2017, 54(16): 68-74.
[10] 李欣然, 姜学皎, 钱军, 等. 基于用户日负荷曲线的用电行业分类与综合方法[J]. 电力系统自动化, 2010, 34(10): 56-61.
Li Xinran, Jiang Xuejiao, Qian Jun, et al.A classifying and synthesizing method of power consumer industry based on the daily load profile[J]. Automation of Electric Power Systems, 2010, 34(10): 56-61.
[11] 杨浩, 张磊, 何潜, 等. 基于自适应模糊C均值算法的电力负荷分类研究[J]. 电力系统保护与控制, 2010, 38(16): 111-115, 122.
Yang Hao, Zhang Lei, He Qian, et al.Study of power load classification based on adaptive fuzzy C means[J]. Power System Protection and Control, 2010, 38(16): 111-115, 122.
[12] Benítez I, Quijano A, Díez J L, et al.Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers[J]. International Journal of Electrical Power & Energy Systems, 2014, 55: 437-448.
[13] 金伟超, 张旭, 刘晟源, 等. 基于剪枝策略和密度峰值聚类的行业典型负荷曲线辨识[J]. 电力系统自动化, 2021, 45(4): 20-28.
Jin Weichao, Zhang Xu, Liu Shengyuan, et al.Identification of typical industrial power load curves based on pruning strategy and density peak clustering[J]. Automation of Electric Power Systems, 2021, 45(4): 20-28.
[14] 赵晋泉, 夏雪, 刘子文, 等. 电力用户用电特征选择与行为画像[J]. 电网技术, 2020, 44(9): 3488-3496.
Zhao Jinquan, Xia Xue, Liu Ziwen, et al.User electricity consumption feature selection and behavioral portrait[J]. Power System Technology, 2020, 44(9): 3488-3496.
[15] Haben S, Singleton C, Grindrod P.Analysis and clustering of residential customers energy behavioral demand using smart meter data[J]. IEEE Transactions on Smart Grid, 2016, 7(1): 136-144.
[16] 王潇笛, 刘俊勇, 刘友波, 等. 采用自适应分段聚合近似的典型负荷曲线形态聚类算法[J]. 电力系统自动化, 2019, 43(1): 110-118.
Wang Xiaodi, Liu Junyong, Liu Youbo, et al.Shape clustering algorithm of typical load curves based on adaptive piecewise aggregate approximation[J]. Automation of Electric Power Systems, 2019, 43(1): 110-118.
[17] 徐春华, 陈克绪, 马建, 等. 基于深度置信网络的电力负荷识别[J]. 电工技术学报, 2019, 34(19): 4135-4142.
Xu Chunhua, Chen Kexu, Ma Jian, et al.Recognition of power loads based on deep belief network[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4135-4142.
[18] Wang Yi, Chen Qixin, Kang Chongqing, et al.Clustering of electricity consumption behavior dynamics toward big data applications[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2437-2447.
[19] 孙毅, 毛烨华, 李泽坤, 等. 面向电力大数据的用户负荷特性和可调节潜力综合聚类方法[J]. 中国电机工程学报, 2021, 41(18): 6259-6271.
Sun Yi, Mao Yehua, Li Zekun, et al.A comprehensive clustering method of user load characteristics and adjustable potential based on power big data[J]. Proceedings of the CSEE, 2021, 41(18): 6259-6271.
[20] 庞传军, 余建明, 冯长有, 等. 基于LSTM自动编码器的电力负荷聚类建模及特性分析[J]. 电力系统自动化, 2020, 44(23): 57-63.
Pang Chuanjun, Yu Jianming, Feng Changyou, et al.Clustering modeling and characteristic analysis of power load based on long-short-term-memory auto-encoder[J]. Automation of Electric Power Systems, 2020, 44(23): 57-63.
[21] 卢锦玲, 郭鲁豫. 基于改进深度残差收缩网络的电力系统暂态稳定评估[J]. 电工技术学报, 2021, 36(11): 2233-2244.
Lu Jinling, Guo Luyu.Power system transient stability assessment based on improved deep residual shrinkage network[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2233-2244.
[22] 张斌, 庄池杰, 胡军, 等. 结合降维技术的电力负荷曲线集成聚类算法[J]. 中国电机工程学报, 2015, 35(15): 3741-3749.
Zhang Bin, Zhuang Chijie, Hu Jun, et al.Ensemble clustering algorithm combined with dimension reduction techniques for power load profiles[J]. Proceedings of the CSEE, 2015, 35(15): 3741-3749.
[23] Xie Junyuan, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis[EB/OL].2015, arXiv: 1511.06335. https://arxiv.org/abs/1511.06335.
[24] London Government.Smart meter energy use data in London households[DB/OL]. [2015-09-26].https://old.datahub.io/dataset/smartmeter-energy-use-data-in-london-households.
[25] Alonso A M, Nogales F J, Ruiz C.Hierarchical clustering for smart meter electricity loads based on quantile autocovariances[J]. IEEE Transactions on Smart Grid, 2020, 11(5): 4522-4530.
[26] Van Der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2625.
[27] Commission for Energy Regulation. CER smart metering project[DB/OL].[2012-03-01]. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/.