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A parametric bootstrap algorithm for cluster number determination of load pattern categorization

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  • Luo, Xing
  • Zhu, Xu
  • Lim, Eng Gee

Abstract

The latest development of smart grid technologies gives rise to big load data and requires load pattern categorization (LPC). How to determine a precise cluster number and choose an appropriate clustering algorithm are critical and still remain challenging in LPC. In this work, we propose a novel parametric bootstrap (PB) algorithm to address the cluster number determination problem in load pattern analysis. The proposed PB algorithm is more robust against dimensionality of data and more applicable for the load demand data which is usually of high dimensionality. The PB algorithm is also general and independent of data type, resulting in a more precise cluster number determined than existing methods with little fluctuation. Moreover, an effective cascade clustering scheme is proposed to categorize load demand data and analyze load patterns, based on the PB algorithm and the K-means++ clustering algorithm. The results indicate the feasibility and the superiority of the proposed approach.

Suggested Citation

  • Luo, Xing & Zhu, Xu & Lim, Eng Gee, 2019. "A parametric bootstrap algorithm for cluster number determination of load pattern categorization," Energy, Elsevier, vol. 180(C), pages 50-60.
    • Handle: RePEc:eee:energy:v:180:y:2019:i:c:p:50-60
    DOI: 10.1016/j.energy.2019.04.089
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    References listed on IDEAS

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    Cited by:

    1. Valdes, Javier & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2021. "Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications," Energy, Elsevier, vol. 215(PA).
    2. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2022. "Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants," Renewable Energy, Elsevier, vol. 185(C), pages 1062-1077.
    3. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.
    4. Yuya Tanigawa & Narayanan Krishnan & Eitaro Oomine & Atushi Yona & Hiroshi Takahashi & Tomonobu Senjyu, 2023. "Clustering Method for Load Demand to Shorten the Time of Annual Simulation," Energies, MDPI, vol. 16(5), pages 1-22, February.
    5. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).

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