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Load Classification and Driven Factors Identification Based on Ensemble Clustering

In: Smart Energy Management

Author

Listed:
  • Kaile Zhou

    (Hefei University of Technology)

  • Lulu Wen

    (Hefei University of Technology)

Abstract

A two-stage framework is presented for residential load classification and driven factors identification in this chapter. In the first stage, a combined method of k-means and spectral clustering (CKSC) is first used for load classification. In the second stage called driven factors identification, different residential load patterns in weekdays and weekends are first analyzed. Then driven factors behind each load pattern are investigated based on multi-nominal logistic regression. Experiment is carried out on the Irish open data, including both detailed smart meter measurements in a 30-min time interval and the survey data of 4,181 households. It shows that CKSC method obtains clustering results from the load profile data with high efficiency. Households with different load patterns are efficiently divided into several groups. The driven factors behind each pattern are found and discussed further. Knowledge discovered from load classification and driven factors identification can support better understanding of residential energy use and thus support the development of targeted energy service strategies.

Suggested Citation

  • Kaile Zhou & Lulu Wen, 2022. "Load Classification and Driven Factors Identification Based on Ensemble Clustering," Springer Books, in: Smart Energy Management, chapter 0, pages 81-99, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-9360-1_4
    DOI: 10.1007/978-981-16-9360-1_4
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