Open Access
Issue
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
DOI https://doi.org/10.1051/matecconf/202235502024
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. [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.] [Google Scholar]

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