Residential Electricity Consumption Pattern Mining Based on Fuzzy Clustering

Kaile Zhou () and Lulu Wen ()
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Kaile Zhou: Hefei University of Technology
Lulu Wen: Hefei University of Technology

Chapter Chapter 2 in Smart Energy Management, 2022, pp 33-50 from Springer

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-9360-1_2

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DOI: 10.1007/978-981-16-9360-1_2

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