Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment
<p dir="ltr">The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and effi...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2023
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| _version_ | 1864513564717350912 |
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| author | Farhat Mahmood (15468854) |
| author2 | Rajesh Govindan (15468857) Amine Bermak (1895947) David Yang (5570408) Tareq Al-Ansari (9872268) |
| author2_role | author author author author |
| author_facet | Farhat Mahmood (15468854) Rajesh Govindan (15468857) Amine Bermak (1895947) David Yang (5570408) Tareq Al-Ansari (9872268) |
| author_role | author |
| dc.creator.none.fl_str_mv | Farhat Mahmood (15468854) Rajesh Govindan (15468857) Amine Bermak (1895947) David Yang (5570408) Tareq Al-Ansari (9872268) |
| dc.date.none.fl_str_mv | 2023-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.apenergy.2023.121190 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Data-driven_robust_model_predictive_control_for_greenhouse_temperature_control_and_energy_utilisation_assessment/22821059 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Built environment and design Building Engineering Electrical engineering Robust model predictive control Artificial neural network Temperature control Particle swarm optimisation Energy assessment Greenhouse |
| dc.title.none.fl_str_mv | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and efficient greenhouse management. However, it does not account for the inaccuracies and uncertainties existing in the system, leading to sub-optimal temperatures. Therefore, this study proposes a comprehensive data-driven robust model predictive control framework for greenhouse temperature control and its energy utilisation assessment in the presence of uncertainties. First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. The artificial neural network demonstrates a higher prediction accuracy and is used as the system model in the proposed control framework. A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. Results illustrate that in the presence of uncertainties, the robust model predictive control strategy outperforms the existing greenhouse climate management system and basic model predictive control with an RMSE of 0.32 °C and 0.60 °C for a two-day simulation period in winter and summer, respectively. Furthermore, the robust model predictive control strategy leads to an energy reduction of 9.67% and 23.61% in winter and summer. The proposed framework is flexible and general and can be applied to other greenhouses with different configurations and cultivated crops by fine-tuning it on the new data set.</p><h2>Other information</h2><p dir="ltr">Published in: Applied Energy<br>License:<a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher'swebsite: <a href="https://doi.org/10.1016/j.apenergy.2023.121190" target="_blank">https://doi.org/10.1016/j.apenergy.2023.121190</a><br><a href="http://dx.doi.org/10.2147/pgpm.s391394" target="_blank"></a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a08b46b5a02a96b8767865abd98a0b9c |
| identifier_str_mv | 10.1016/j.apenergy.2023.121190 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22821059 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessmentFarhat Mahmood (15468854)Rajesh Govindan (15468857)Amine Bermak (1895947)David Yang (5570408)Tareq Al-Ansari (9872268)Built environment and designBuildingEngineeringElectrical engineeringRobust model predictive controlArtificial neural networkTemperature controlParticle swarm optimisationEnergy assessmentGreenhouse<p dir="ltr">The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and efficient greenhouse management. However, it does not account for the inaccuracies and uncertainties existing in the system, leading to sub-optimal temperatures. Therefore, this study proposes a comprehensive data-driven robust model predictive control framework for greenhouse temperature control and its energy utilisation assessment in the presence of uncertainties. First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. The artificial neural network demonstrates a higher prediction accuracy and is used as the system model in the proposed control framework. A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. Results illustrate that in the presence of uncertainties, the robust model predictive control strategy outperforms the existing greenhouse climate management system and basic model predictive control with an RMSE of 0.32 °C and 0.60 °C for a two-day simulation period in winter and summer, respectively. Furthermore, the robust model predictive control strategy leads to an energy reduction of 9.67% and 23.61% in winter and summer. The proposed framework is flexible and general and can be applied to other greenhouses with different configurations and cultivated crops by fine-tuning it on the new data set.</p><h2>Other information</h2><p dir="ltr">Published in: Applied Energy<br>License:<a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher'swebsite: <a href="https://doi.org/10.1016/j.apenergy.2023.121190" target="_blank">https://doi.org/10.1016/j.apenergy.2023.121190</a><br><a href="http://dx.doi.org/10.2147/pgpm.s391394" target="_blank"></a></p>2023-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.apenergy.2023.121190https://figshare.com/articles/journal_contribution/Data-driven_robust_model_predictive_control_for_greenhouse_temperature_control_and_energy_utilisation_assessment/22821059CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/228210592023-08-01T00:00:00Z |
| spellingShingle | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment Farhat Mahmood (15468854) Built environment and design Building Engineering Electrical engineering Robust model predictive control Artificial neural network Temperature control Particle swarm optimisation Energy assessment Greenhouse |
| status_str | publishedVersion |
| title | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| title_full | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| title_fullStr | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| title_full_unstemmed | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| title_short | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| title_sort | Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment |
| topic | Built environment and design Building Engineering Electrical engineering Robust model predictive control Artificial neural network Temperature control Particle swarm optimisation Energy assessment Greenhouse |