Open Access
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
Article Number 02024
Number of page(s) 9
Section Mathematical Science and Application
Published online 12 January 2022
  1. Y Liu, C Yuen,H Huang, et a1. Peak-to-Average ratio constrai ned demand-side management with consumer’s preference in resi dential smart grid. Selected Topics in Signal Processing, IEEE Journal of 8. 1084–1097. 10.1109/JSTSP.2014.2332301. [Google Scholar]
  2. Y Liu, C Sun, Z Niu, et a1. Study on cl assification technology of power load characteristics based on im proved fuzzy C-Means clustering algorithm[J]. Electrical Measure ment & Instrumentation,2014,51(18):5–9. [Google Scholar]
  3. T Zhang, M Gu.Overview of power user load pattern extraction technology and its application[J]. Power System Technology, 2016,40(03):804–811. [Google Scholar]
  4. TLi, H Yang, Y Gao. Overview of household electric l oad identification technology for smart meters[J]. Supply and Elec tricity, 2011,28(06):39–42. [Google Scholar]
  5. Y Liu, Y Liu, L Xu. High performance back propag ation neural network algorithm for massive load data classificatio n[J]. Automation of Electric Power Systems, 2018,42(21):96–105. D OI:10.7500/AEPS20171215005. [Google Scholar]
  6. L Shi, R Zhou, W Zhang, et a1. Load classifica tion method using deep learning and multidimensional fuzzy C-means clustering [J]. Journal of Electric Power System and Auto mation, 2019,31(07 ):43–50. [Google Scholar]
  7. WLi, B Zhou, N Lin. Daily load characteristic curve classification and short-term load forecasting based on fuzzy clu stering and improved BP algorithm[J].Power System Protection a nd Control, 2012,40(03):56–60. [Google Scholar]
  8. S Lin, E Tian, YFu, et a1. A load classification me thod based on information entropy segmentation aggregation appr oximation and apectral clustering[J].Proceedings of the CSEE, 2017, 37(08): 2242–2253. [Google Scholar]
  9. FBu, J Chen, Q Zhang, et a1. A controllable and r efined recognition method for load patterns based on two-layer it erative clustering analysis[J]. Power System Technology, 2018,42(03):903–913. [Google Scholar]
  10. X Wang, Z Chen, X Peng. A load pattern c ombination recognition method based on two-layer clustering analysis[J]. Power System Technology, 2016, 40(05): 1495–1501. [Google Scholar]
  11. S Wang, T Liu. Classification and recognition me thod of resident load gradient lifting tree considering power cons umption mode[J]. Proceedings of the CSUEPSA,2017, 29(09): 27–3 3. [Google Scholar]
  12. X Peng, J Lai, W Chen. Intelligent identification method of customer power consumption mode based on cluster a nalysis[J]. Power System Protection and Control, 2014, 42(19): 68–73. [Google Scholar]
  13. H Jia, GHe, C Fang, et al. Multi-level clu stering method for hierarchical clustering and bidirectional cappin g combination for load forecasting[J]. Power System Technology, 2007(23):33–36. [Google Scholar]
  14. XLi, X Jiang, J Qian, et al. Classification and com prehensive method of power industry based on user daily load c urve[J]. Automation of Electric Power Systems, 2010, 34(10): 56–61. [Google Scholar]
  15. B Zhang, C Zhuang, Hu J, et a1. Integrated clustering a lgorithm for power load curve combined with dimensionality red uction technology[J]. Proceedings of the CSEE, 2015, 35(15): 3741–3749. [Google Scholar]
  16. KLi, ZMa, R Duane, et a1. Identification of typical building daily electricity usage profiles using Gaussian mi xture model-based clustering and hierarchical clustering[J]. Applie d Energy, 2018, 231:331–342. [CrossRef] [Google Scholar]
  17. B Zeng, J Zhang, L Ding, et al. Application of improv ed adaptive fuzzy C-means algorithm in load characteristic classi fication[J]. Automation of Electric Power Systems,2011, 35(12): 42–46. [Google Scholar]
  18. H Yang, L Zhang, QHe, et al. A study of power load cl assification based on adaptive fuzzy C-Means algorithm[J].Power System Protection and Control,2010,38(16):111115+122. [Google Scholar]
  19. MAKONIN S, ELLERT B, BAJIC I V, et al. Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014[J]. Scientific Data, 2016, 3(160037):1–12.] [CrossRef] [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.