Dou, T., Kaszubowski Lopes, Y., Rockett, P. orcid.org/0000-0002-4636-7727 et al. (2 more authors) (2020) Model predictive control of non-domestic heating using genetic programming dynamic models. Applied Soft Computing, 97 (Part B). 106695. ISSN 1568-4946
Abstract
We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the large-scale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate this process, and have performed open-loop system identification over the data produced by an industry grade building simulator. The simulated building was subject to an amplitude modulated pseudo-random binary sequence (APRBS), which allows the collected data to be sufficiently informative to capture the underlying system dynamics under relevant operating conditions.
In this initial report, we detail how we employed GP to construct the predictive model for MPC for heating a single-zone building in simulation, and report results of using this model for controlling the internal environmental conditions of the simulated single-zone building. We conclude that GP shows great promise for producing models that allow the MPC of building to achieve the desired temperature band in a single zone space.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. This is an author produced version of a paper subsequently published in Applied Soft Computing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Genetic programming; Dynamic non-linear system identification; Model predictive control; Building energy management |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/N022351/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Sep 2020 06:16 |
Last Modified: | 26 Jan 2022 13:59 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.asoc.2020.106695 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165101 |