Privacy-Preserving Distributed Clustering for Electrical Load Profiling

Abstract

Electrical load profiling supports retailers in identifying consumer categories for customizing tariff design. However, each retailer only has access to the data of the customers it serves. Centralized joint clustering on retailers' union load dataset either enables the identification of more types of users that allows to design more customized retail plans, or informs whether each retailer already has a sufficiently broad customer base. However, the centralized clustering requires access to the confidential data of retailers. This may cause privacy issues among retailers, because retailers can not or do not want to share their confidential information with others. To tackle this issue, we propose a privacy-preserving distributed clustering framework by developing a privacy-preserving accelerated average consensus (PP-AAC) algorithm. 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, the clustering on retailers' union dataset can be achieved only by local calculations and information sharing between neighboring retailers without sacrificing privacy. The correctness, privacy-preserving property, time-saving feature, and robustness to random communication failures of the proposed methods are verified using a real-world Irish residential dataset.