Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160297
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dc.contributor.authorYang, Shiyuen_US
dc.contributor.authorWan, Man Punen_US
dc.contributor.authorChen, Wanyuen_US
dc.contributor.authorNg, Bing Fengen_US
dc.contributor.authorDubey, Swapnilen_US
dc.date.accessioned2022-07-19T02:11:16Z-
dc.date.available2022-07-19T02:11:16Z-
dc.date.issued2021-
dc.identifier.citationYang, S., Wan, M. P., Chen, W., Ng, B. F. & Dubey, S. (2021). Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control. Applied Energy, 288, 116648-. https://dx.doi.org/10.1016/j.apenergy.2021.116648en_US
dc.identifier.issn0306-2619en_US
dc.identifier.urihttps://hdl.handle.net/10356/160297-
dc.description.abstractThe adoption of model predictive control (MPC) for building automation and control applications is challenged by the high hardware and software requirements to solve its optimization problem. This study proposes an approximate MPC that mimics the dynamic behaviours of MPC using the recurrent neural network with a structure of nonlinear autoregressive network with exogenous inputs. The approximate MPC is developed by learning from the measured operation data of buildings controlled by MPC, therefore it can produce MPC-like control for buildings without needing to solve the optimization problem, significantly reducing the computation load as compared to MPC. The proposed approximate MPC is implemented in two testbeds, an office and a lecture theatre, to control the air-conditioning systems. The control performance of the approximate MPC is compared to MPC as well as the original reactive control of the two testbeds. The approximate MPC retained most of the energy and thermal comfort performance of MPC in both testbeds. For the office, the MPC and approximate MPC reduced 58.5% and 51.6% of cooling energy consumption, respectively, as compared to the original control. For the lecture theatre, the MPC and approximate MPC reduced 36.7% and 36.2% of cooling energy consumption, respectively, as compared to the original control. Meanwhile, both approximate MPC and MPC significantly improved indoor thermal comfort in the two testbeds as compared to their original control. Despite having minor degradation in control performance the approximate MPC was more than 100 times faster than MPC in generating optimal control commands in each time step.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationN190107T00en_US
dc.relation2019-0607en_US
dc.relationNRF2016IDM-TRANS001-031en_US
dc.relation.ispartofApplied Energyen_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleExperiment study of machine-learning-based approximate model predictive control for energy-efficient building controlen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.identifier.doi10.1016/j.apenergy.2021.116648-
dc.identifier.scopus2-s2.0-85101158006-
dc.identifier.volume288en_US
dc.identifier.spage116648en_US
dc.subject.keywordsModel Predictive Controlen_US
dc.subject.keywordsMachine-Learningen_US
dc.description.acknowledgementThis research is financially supported by JTC Corporation (contract nos. N190107T00 and 2019-0607) and Smart Nation & Digital Government Office (SNDGO) of Singapore (Grant no. NRF2016IDM-TRANS001-031).en_US
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