Load Profiling Considering Shape Similarity Using Shape-Based Clustering

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

Chapter Chapter 3 in Smart Energy Management, 2022, pp 51-79 from Springer

Abstract: Abstract In this chapter, it presents an improved K-means algorithm with optimized initial cluster centers to recognize electricity consumption patterns. In the algorithm, principal component analysis (PCA) is first used to reduce the dimensions of smart meter time series data and thus to accelerate the process of clustering while guaranteeing the clustering quality. Daily electricity consumption profiles (ECPs), including both from the smart metering electricity customer behavior trials of Irish, and the yearly residential ECPs from real world, are used in the experiment. The ECPs are divided into different clusters and the characteristics of each cluster are extracted. However, the changes of residential electricity consumption are also reflected in the shape variation of ECPs. Traditional distance cannot find the shape similarity of ECPs. Therefore, it uses a shape-based clustering method to group electricity consumption profiles with similar shapes, and the detailed algorithm procedures are provided. The results show that the shape-based clustering method can effectively find similar shapes and recognize typical electricity consumption patterns based on daily ECPs. The load profiling and pattern recognition of residential electricity consumption in smart grid environment is important for supporting effective demand side management and improving energy utilization efficiency.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-9360-1_3

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DOI: 10.1007/978-981-16-9360-1_3

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