Open Access
E3S Web Conf.
Volume 213, 2020
2nd International Conference on Applied Chemistry and Industrial Catalysis (ACIC 2020)
Article Number 02040
Number of page(s) 5
Section Energy Mining Research and Composite Material Performance Analysis
Published online 01 December 2020
  1. Zhao Yang, Hu Shiyao, Yang Shuqiang, et al. Analysis of data-driven based users’ electricity consumption behaviour in retail market [J]. Power Demand Side Management, 2020, 22 (04) : 45-50. [Google Scholar]
  2. Hao Ran, Ai Qian, Xiao Fei. Architecture based on multivariate big data platform for analysing electricity consumption behaviour [J]. Electric Power Automation Equipment, 2017, 37 (8) : 20-27. [Google Scholar]
  3. Gong Gangjun, Chen Zhimin, Lu Jun, et al. Clustering Optimization Strategy for Electricity Consumption Behaviour Analysis in Smart Grid [J]. Automation of Electric Power Systems, 2018, 42 (2) : 58-63. [Google Scholar]
  4. Wang Qixin, Liu Dichen, Wu Jun, et al. Comprehensive optimization including user behaviour analysis for supply and demand sides of IES-MEC [J]. Power Automation Equipment, 2017, 37 (6) : 179-185. [Google Scholar]
  5. Jin Wende, Jiang Yibao, Ding Yi. Study on Critical Issues Correlated to Optimization and Management in Customer-Centered Integrated Energy System and the Prospect [J]. Zhejiang Electric Power, 2016, 35 (10) : 73-80. [Google Scholar]
  6. Ma Tiannan, Wang Chao, Peng Lilin, et al. Predictive analysis of energy consumption behaviour of integrated energy system users under multi-heterogeneous big data[J]. Power System Analysis and Research, 2018, 46 (10) : 86-95. [Google Scholar]
  7. Liu Sifang, Deng Chunyu, Zhang Guobin, et al. Research on collaborative architecture for edge computing of residential intelligent usage of electricity[J]. Electric Power Construction, 2018, 39(11) : 69-77 (in Chinese) [Google Scholar]
  8. Henriet, Simon & Simsekli, Umut & Fuentes, Benoit & Richard, Gaël. A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings[J]. arXiv e-prints, 2018, arXiv : 1803.00515. [Google Scholar]
  9. Xu Changqing, Zhao Huadong, Song Xiaohui. Research on method of power user group identification and analysis based on large data[J]. Journal of Zhengzhou University, 2016, 48(3): 113-117 (in Chinese) [Google Scholar]
  10. Chévez, Pedro, Barbero D, Martini I, et al. Application of the K-means clustering method for the detection and analysis of areas of homogeneous residential electricity consumption at the Great La Plata region, Buenos Aires, Argentina[J]. Sustainable Cities and Society, 2017(32): 115-129. [CrossRef] [Google Scholar]
  11. Liu Yang, Xu Lixiong. High-performance back propagation neural network algorithm for classification of mass load data[J]. Automation of Electric Power Systems, 2018, 42(21) : 131-140 (in Chinese). [Google Scholar]
  12. Oprea S, Bâra A. Electricity load profile calculation using self-organizing maps[C]//20th International Conference on System Theory, Control and Computing (ICSTCC). Sinaia: ICSTCC, 2016: 860-865. [Google Scholar]
  13. Quilumba F L, Lee W J, Huang H, et al. Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 911-918. [CrossRef] [Google Scholar]
  14. Zhao Teng, Wang Lintong, Zhang Yan, et al. Relation factor identification of electricity consumption behavior of users and electricity demand forecasting based on mutual information and random forests[J]. Proceedings of the CSEE, 2016, 36(3): 604-614 (in Chinese). [Google Scholar]
  15. Tongzhi L I. Technical implications and development trends of flexible and interactive utilization of intelligent power[J]. Automation of Electric Power Systems, 2012, 36(2): 11-17. [Google Scholar]
  16. Yang Xuying, Zhou Ming, Li Gengyin. Survey on demand response mechanism and modeling in smart grid[J]. Power System Technology, 2016, 40(1): 220-226 (in Chinese). [Google Scholar]
  17. Cai Long, Gu Jie, Jin Zhijian. Study on factor identification and feature extraction of residential demand response behavior[J]. Power System Technology2017, 41(7): 346-353 (in Chinese). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.