2020/02/26 by Mengshuo Jia, Yi Wang, Jia, Mengshuo +5 · 1 citation
Computer Science · Engineering · Social Sciences · #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Human Mobility and Location-Based Analysis #Parallel #Signal Processing (eess.SP) #Smart Grid Energy Management #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.12769
openalex publication_date 2020/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Electrical load profiling supports retailers and distribution network operators in having a better understanding of the consumption behavior of consumers. However, traditional clustering methods for load profiling are centralized and require access to all the smart meter data, thus causing privacy issues for consumers and retailers. To tackle this issue, we propose a privacy-preserving distributed clustering framework for load profiling by developing a privacy-preserving accelerated average consensus (PP-AAC) algorithm with proven convergence. Using the proposed framework, we modify several commonly used clustering methods, including k-means, fuzzy C-means, and Gaussian mixture model, to provide privacy-preserving distributed clustering methods. In this way, load profiling can be performed only by local calculations and information sharing between neighboring data owners without sacrificing privacy. Meanwhile, compared to traditional centralized clustering methods, the computational time consumed by each data owner is significantly reduced. The privacy and complexity of the proposed privacy-preserving distributed clustering framework are analyzed. The correctness, efficiency, effectiveness, and privacy-preserving feature of the proposed framework and the proposed PP-AAC algorithm are verified using a real-world Irish residential dataset.