A deep learning-based method for non-intrusive load monitoring and load disaggregation of 11kV/400V electrical Substations

O’Malley, Elliott, Pugh, Daniel ORCID: https://orcid.org/0000-0002-6721-2265, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Clements, David 2024. A deep learning-based method for non-intrusive load monitoring and load disaggregation of 11kV/400V electrical Substations. Presented at: 18th International Conference on Energy Sustainability, Anaheim, California, 15-17 July 2024. ASME 2024 18th International Conference on Energy Sustainability. ASME 2024 18th International Conference on Energy Sustainability. American Society of Mechanical Engineers, 10.1115/es2024-130477

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Abstract

An Artificial Intelligence-based method has been developed to achieve Non-Intrusive Load Monitoring (NILM) at 11kV/400V electrical substations, accomplished using the aggregate load measured at the substation and separating it by property. This allows Utility & Power Distribution companies better electricity demand forecasts and aids demand-side response. There has been extensive research of NILM at household-level using household “smart meter” data. However, substation-level disaggregation offers a cost-effective alternative and streamlined approach as only the substation requires monitoring devices rather than every house in the distribution region. A deep learning eventless NILM approach was taken, using Recurrent Neural Networks with Long Short-Term Memory and Gated Recurrent Unit layers. Disaggregation was achieved using a synthetic dataset for Low-Carbon Technology devices such as Electric vehicles (EVs), Heat Pumps (HP) and Photo-Voltaic Solar (PV) with the relationship with substation load. The accuracies for unseen data compared to the ground truth were 99.20% (EV), 99.39% (PV), 81.39% (HP) and 92.16% for other household loads. This method proposed in this paper is foundational to future substation-level NILM research, allowing for an evaluation of a scenario-based uptake in low-carbon technologies and an analysis of the required change of the power distribution network.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: https://www.asme.org/publications-submissions/publishing-information/legal-policies, Start Date: 2024-07-15
Publisher:	American Society of Mechanical Engineers
ISBN:	978-0-7918-8789-9
Last Modified:	10 Oct 2024 08:17
URI:	https://orca.cardiff.ac.uk/id/eprint/172778

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