An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Rongheng Lin, Budan Wu and Yun Su
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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

Energies, 2018, vol. 11, issue 9, 1-17

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.

Keywords: smart grid; clustering algorithm; similarity measurement; Pearson similarity; power load data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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