most citedPrivacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

4 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2021★ 1 cited

A Review on Cybersecurity in Smart Local Energy Systems: Requirements, Challenges, and Standards

Siyuan Dong, Jun Cao, Zhong Fan

Smart local energy system (SLES) is considered as a promising pathway facilitating a more effective and localised operation, benefited from the complex information and communicatio…

eess.SY2021

The energy revolution: cyber physical advances and opportunities for smart local energy systems

Nandor Verba, Elena Gaura, Stephen McArthur +7

We have designed a two-stage, 10-step process to give organisations a method to analyse small local energy systems (SLES) projects based on their Cyber Physical System components i…

cs.LG2021★ 4 cited

Federated Learning for Short-term Residential Load Forecasting

Christopher Briggs, Zhong Fan, Peter Andras

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transiti…

cs.LG2020★ 4 cited

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

Christopher Briggs, Zhong Fan, Peter Andras

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 resoluti…

cs.LG2020

A Review of Privacy-preserving Federated Learning for the Internet-of-Things

Christopher Briggs, Zhong Fan, Peter Andras

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 learn…