1 code implementation • 20 Nov 2023 • Matthew Deakin, Marta Vanin, Zhong Fan, Dirk Van Hertem
Power meter data and a network model are shown to be necessary for developing algorithms that enable decision-making that is robust to real-world uncertainties, with possibilities and challenges of Digital Twin development clearly demonstrated.
no code implementations • 26 Aug 2022 • Cephas Samende, Zhong Fan, Jun Cao
Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production.
no code implementations • 1 Apr 2022 • Zhong Fan, Jun Cao, Taskin Jamal, Chris Fogwill, Cephas Samende, Zoe Robinson, Fiona Polack, Mark Ormerod, Sharon George, Adam Peacock, David Healey
We demonstrate the potential role of one of the largest at scale multi-vector Smart Energy Network Demonstrator (SEND).
no code implementations • 21 Nov 2021 • Daniel J. B. Harrold, Jun Cao, Zhong Fan
In this paper, multi-agent reinforcement learning is used to control a hybrid energy storage system working collaboratively to reduce the energy costs of a microgrid through maximising the value of renewable energy and trading.
no code implementations • 20 Aug 2021 • Cephas Samende, Jun Cao, Zhong Fan
In this paper, we investigate an energy cost minimization problem for prosumers participating in peer-to-peer energy trading.
no code implementations • 29 Jun 2021 • Nandor Verba, Elena Gaura, Stephen McArthur, George Konstantopoulos, Jianzhoug Wu, Zhong Fan, Dimitrios Athanasiadis, Pablo Rodolfo Baldivieso Monasterios, Euan Morris, Jeffrey Hardy
SLES are often developed for a specific range of use cases and functions, and these match the specific requirements and needs of the community, location or site under consideration.
no code implementations • 10 Jun 2021 • Daniel J. B. Harrold, Jun Cao, Zhong Fan
As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy.
no code implementations • 27 May 2021 • 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.
no code implementations • 14 Dec 2020 • 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.
no code implementations • 24 Apr 2020 • 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.
no code implementations • 24 Apr 2020 • Christopher Briggs, Zhong Fan, Peter Andras
However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion -- as is typical in real world situations -- the joint model produced by FL suffers in terms of test set accuracy and/or communication costs compared to training on iid data.