  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.
