All Outputs (3)

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (2021)
Presentation / Conference
Fan. (2021, December). Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. Presented at NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's ho... Read More about Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters.

Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning (2021)
Journal Article
Fan. (2022). Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning. Energy, https://doi.org/10.1016/j.energy.2021.121958

As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery... Read More about Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning.

A Review of Privacy-preserving Federated Learning for the Internet-of-Things (2021)
Book Chapter
Fan. (2021). A Review of Privacy-preserving Federated Learning for the Internet-of-Things. In Federated Learning Systems: Towards Next Generation AI (21-50). https://doi.org/10.1007/978-3-030-70604-3_2

The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant pr... Read More about A Review of Privacy-preserving Federated Learning for the Internet-of-Things.