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An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Author

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  • Rongheng Lin

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Budan Wu

    (State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Yun Su

    (State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

Abstract

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering.

Suggested Citation

  • Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2466-:d:170271
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    References listed on IDEAS

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    1. Yikun Zhang & Jing Zhang & Gang Yao & Xiao Xu & Kewen Wei, 2020. "Method for Clustering Daily Load Curve Based on SVD-KICIC," Energies, MDPI, vol. 13(17), pages 1-15, August.

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