A review of reinforcement learning based approaches for industrial demand response

Shah, Margi ORCID: https://orcid.org/0000-0003-2222-8412, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Mowbray, Max 2024. A review of reinforcement learning based approaches for industrial demand response. Presented at: 15th International Conference on Applied Energy (ICAE2023), Qatar,Doha, 3-7 December 2023. Energy Proceedings. , vol.40 10.46855/energy-proceedings-10959

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Abstract

Industrial demand response plays a key role in mitigating the operational challenges of smart grid brought by massive proliferation of distributed energy resources. However, industrial plants have complex and intertwined processes, which provides barriers for their participation in industrial demand response programs. This is in part due to the complexity and uncertainties of approximating systems models. More recently, reinforcement learning has emerged as a data-driven control technique for sequential decision-making under uncertainty. This emergence is strongly coupled with the abundance of data offered by advanced information technologies. The potential of applying reinforcement learning in industrial demand response is identified in this work by comparing pivotal aspects of reinforcement learning with the requirements of industrial demand response schemes.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Engineering
ISSN:	2004-2965
Date of First Compliant Deposit:	15 April 2024
Date of Acceptance:	29 September 2023
Last Modified:	14 May 2024 11:30
URI:	https://orca.cardiff.ac.uk/id/eprint/167943

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