Model predictive control of non-domestic heating using genetic programming dynamic models

Journal article


Mahmoudi Saber, E. (2020). Model predictive control of non-domestic heating using genetic programming dynamic models. Applied Soft Computing . 97 (B), pp. 106695-106695. https://doi.org/10.1016/j.asoc.2020.106695
AuthorsMahmoudi Saber, E.
Abstract

© 2020 Elsevier B.V. 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.

Year2020
JournalApplied Soft Computing
Journal citation97 (B), pp. 106695-106695
PublisherElsevier BV
ISSN1568-4946
Digital Object Identifier (DOI)https://doi.org/10.1016/j.asoc.2020.106695
Web address (URL)http://dx.doi.org/10.1016/j.asoc.2020.106695
Publication dates
Online05 Sep 2020
PrintDec 2020
Sep 2020
Publication process dates
Accepted26 Aug 2020
Deposited27 May 2021
Accepted author manuscript
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File Access Level
Open
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