Wei, H.-L. orcid.org/0000-0002-4704-7346 (2023) System identification-informed transparent and explainable machine learning with application to power consumption forecasting. In: 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) Proceedings. 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 19-21 Jul 2023, Tenerife, Canary Island, Spain. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9798350322989
Abstract
System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) Proceedings is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | system identification; machine learning; transparent model; explainable model; power consumption |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/I011056/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/H00453X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jul 2023 15:59 |
Last Modified: | 25 Sep 2023 13:52 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ICECCME57830.2023.10252535 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201102 |
Download
Filename: System Identification-Informed Machine Learning Final Accepted Manuscript.pdf
Licence: CC-BY 4.0