Smart Metering System: Developing New Designs to Improve Privacy and Functionality

Research output: ThesisDoctoral Thesis


This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase the
predictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project.
Original languageEnglish
Awarding Institution
  • Royal Holloway, University of London
  • Kuenzel, Stefanie, Supervisor
  • Watkins, Chris, Advisor
  • Córdoba-Pachón, José-Rodrigo , Advisor
Thesis sponsors
Award date1 Jul 2022
Publication statusUnpublished - 2022

Cite this