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Residential Electricity Consumption Pattern Mining Based on Fuzzy Clustering

In: Smart Energy Management

Author

Listed:
  • Kaile Zhou

    (Hefei University of Technology)

  • Lulu Wen

    (Hefei University of Technology)

Abstract

In this chapter, an improved fuzzy clustering model is used for residential electricity consumption pattern mining. First, the background of clustering and fuzzy c-means clustering is introduced. Then a process model of residential electricity consumption pattern mining and an improved fuzzy c-means clustering model are provided. Three key aspects of the improved fuzzy c-means clustering model, namely fuzzifier selection, cluster validation and searching capability optimization, are discussed. Finally, the real residential daily electricity consumption data during a month are used in the experiment. With the presented model, different groups are obtained and the characteristics of each group are extracted. The results reveal the different electricity consumption patterns of different households and demonstrate the effectiveness of the clustering-based model. The customer segmentation based on electricity consumption pattern mining plays a key role in supporting the development of personalized and targeted marketing strategies and the improvement of energy efficiency.

Suggested Citation

  • Kaile Zhou & Lulu Wen, 2022. "Residential Electricity Consumption Pattern Mining Based on Fuzzy Clustering," Springer Books, in: Smart Energy Management, chapter 0, pages 33-50, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-9360-1_2
    DOI: 10.1007/978-981-16-9360-1_2
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