26
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Incremental segmented slope residential load pattern clustering based on three-stage curve profiles

, , , , &
Pages 263-282 | Received 09 Jun 2023, Accepted 20 Sep 2023, Published online: 03 Oct 2023

References

  • Li H, Wang Z, Hong T, et al. Characterizing patterns and variability of building electric load profiles in time and frequency domains. Appl Energy. 2021;291:116721. doi: 10.1016/j.apenergy.2021.116721
  • Nizar AH, Dong ZY, Wang Y. Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst. 2008;23(3):946–955. doi: 10.1109/TPWRS.2008.926431
  • Chaouch M. Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves. IEEE Trans Smart Grid. 2014;5(1):411–419. doi: 10.1109/TSG.2013.2277171
  • Chicco G, Napoli R, Postolache P, et al. Customer characterization options for improving the tariff offer. IEEE Trans Power Syst. 2003;18(1):381–387. doi: 10.1109/TPWRS.2002.807085
  • Majid MS, Rahman HA, Hassan MY et al. Demand side management using direct load control for residential. In: 2006 4th Student Conference on Research and Development; Shah Alam, Malaysia; 2006. p. 241–245.
  • Chang R, Lu C Load profiling and its applications in power market. In: 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491); Toronto, ON, Canada; Vol. 2; 2003. p. 974–978.
  • Mohajeryami S, Schwarz P, Baboli PT Including the behavioral aspects of customers in demand response model: real time pricing versus peak time rebate. In: 2015 North American Power Symposium (NAPS); Charlotte, NC, USA; 2015. p. 1–6.
  • Lin S, Li F, Tian E, et al. Clustering load profiles for demand response applications. IEEE Trans Smart Grid. 2019;10(2):1599–1607. doi: 10.1109/TSG.2017.2773573
  • Alvarez MAZ, Agbossou K, Cardenas A, et al. Demand response strategy applied to residential electric water heaters using dynamic programming and k-means clustering. IEEE Trans Sustainable Energy. 2020;11(1):524–533. doi: 10.1109/TSTE.2019.2897288
  • Choksi KA, Jain S, Pindoriya NM. Feature based clustering technique for investigation of domestic load profiles and probabilistic variation assessment: smart meter dataset. Sust Energy Grids Networks. 2020;22:100346. doi: 10.1016/j.segan.2020.100346
  • Luo X, Zhu X, Lim EG. A parametric bootstrap algorithm for cluster number determination of load pattern categorization. Energy. 2019;180:50–60. doi: 10.1016/j.energy.2019.04.089
  • Alonso AM, Nogales FJ, Ruiz C. Hierarchical clustering for smart meter electricity loads based on quantile autocovariances. IEEE Trans Smart Grid. 2020;11(5):4522–4530. doi: 10.1109/TSG.2020.2991316
  • Alvarez MAZ, Agbossou K, Cardenas A, et al. Demand response strategy applied to residential electric water heaters using dynamic programming and k-means clustering. IEEE Trans Sustainable Energy. 2019;11(1):524–533. doi: 10.1109/TSTE.2019.2897288
  • Azaza M, Wallin F. Smart meter data clustering using consumption indicators: responsibility factor and consumption variability. Energy Procedia. 2017;142:2236–2242. doi: 10.1016/j.egypro.2017.12.624
  • Li K, Cao X, Ge X, et al. Meta-heuristic optimization-based two-stage residential load pattern clustering approach considering intra-cluster compactness and inter-cluster separation. IEEE Trans Sustain Energy. 2020;56(4):3375–3384.
  • Rajabi A, Eskandari M, Ghadi MJ, et al. A comparative study of clustering techniques for electrical load pattern segmentation. Renew Sust Energ Rev. 2020;120:109628. doi: 10.1016/j.rser.2019.109628
  • Wang J, Wang K, Jia R et al. Research on load clustering based on singular value decomposition and k-means clustering algorithm. In: 2020 Asia Energy and Electrical Engineering Symposium (AEEES); Chengdu, China; 2020. p. 831–835.
  • Tan X, Feng W, Shi G et al. Application of trajectory clustering on improved artificial intelligence load forecasting. In: 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC); Chengdu, China; Vol. 1; IEEE; 2019. p. 47–51.
  • Wang X, Chen Z, Peng X. A new combinational electrical load analysis method based on bilayer clustering analysis. Power Syst Technol. 2016;40(5):1495–1501.
  • Li R, Li F, Smith ND. Load characterization and low-order approximation for smart metering data in the spectral domain. IEEE Trans Ind Inform. 2016;13(3):976–984. doi: 10.1109/TII.2016.2638319
  • Jiang Z, Lin R, Yang F, et al. A fused load curve clustering algorithm based on wavelet transform. IEEE Trans Ind Inform. 2018;14(5):1856–1865. doi: 10.1109/TII.2017.2769450
  • Xiang Y, Hong J, Yang Z, et al. Slope-based shape cluster method for smart metering load profiles. IEEE Trans Smart Grid. 2020;11(2):1809–1811. doi: 10.1109/TSG.2020.2965801
  • Zhong S, Tam KS. A frequency domain approach to characterize and analyze load profiles. IEEE Trans Power Syst. 2012;27(2):857–865. doi: 10.1109/TPWRS.2011.2170592

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.