Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
<p dir="ltr">With the global increase in food demand, closed and controlled greenhouses are an essential source for year-round crop production. Maintaining the optimum conditions inside the greenhouse throughout the year is critical to improving crop quality and yield. However, green...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513549502513152 |
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| author | Farhat Mahmood (15468854) |
| author2 | Rajesh Govindan (15468857) Amine Bermak (1895947) David Yang (5570408) Carol Khadra (17191699) Tareq Al-Ansari (9872268) |
| author2_role | author author author author author |
| author_facet | Farhat Mahmood (15468854) Rajesh Govindan (15468857) Amine Bermak (1895947) David Yang (5570408) Carol Khadra (17191699) 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) Carol Khadra (17191699) Tareq Al-Ansari (9872268) |
| dc.date.none.fl_str_mv | 2021-11-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jclepro.2021.129172 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Energy_utilization_assessment_of_a_semi-closed_greenhouse_using_data-driven_model_predictive_control/24339928 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agriculture, land and farm management Food sciences Information and computing sciences Data management and data science Model predictive control Energy saving Greenhouse Agriculture Food |
| dc.title.none.fl_str_mv | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">With the global increase in food demand, closed and controlled greenhouses are an essential source for year-round crop production. Maintaining the optimum conditions inside the greenhouse throughout the year is critical to improving crop quality and yield. However, greenhouses consume more resources than other commercial buildings due to their inefficient operation and structure. Therefore, a data-driven model predictive control approach for a semi-closed greenhouse is proposed for temperature control and reducing energy consumption in this study. The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. The greenhouse model's performance is evaluated under varying scenarios, such as increasing the prediction time step and changing the number of samples in the training data set. Results illustrated that the MPC approach had a better temperature control than the greenhouse adaptive control system for winter and summer with an RMSE value of 0.33 °C and 0.36 °C, respectively. Similarly, model predictive control resulted in an energy reduction of 7.70% for winter and 16.57% for the summer season. The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Cleaner Production<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jclepro.2021.129172" target="_blank">https://dx.doi.org/10.1016/j.jclepro.2021.129172</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8261f761ea5b39c54065a4a43f79c2e2 |
| identifier_str_mv | 10.1016/j.jclepro.2021.129172 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24339928 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive controlFarhat Mahmood (15468854)Rajesh Govindan (15468857)Amine Bermak (1895947)David Yang (5570408)Carol Khadra (17191699)Tareq Al-Ansari (9872268)Agricultural, veterinary and food sciencesAgriculture, land and farm managementFood sciencesInformation and computing sciencesData management and data scienceModel predictive controlEnergy savingGreenhouseAgricultureFood<p dir="ltr">With the global increase in food demand, closed and controlled greenhouses are an essential source for year-round crop production. Maintaining the optimum conditions inside the greenhouse throughout the year is critical to improving crop quality and yield. However, greenhouses consume more resources than other commercial buildings due to their inefficient operation and structure. Therefore, a data-driven model predictive control approach for a semi-closed greenhouse is proposed for temperature control and reducing energy consumption in this study. The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. The greenhouse model's performance is evaluated under varying scenarios, such as increasing the prediction time step and changing the number of samples in the training data set. Results illustrated that the MPC approach had a better temperature control than the greenhouse adaptive control system for winter and summer with an RMSE value of 0.33 °C and 0.36 °C, respectively. Similarly, model predictive control resulted in an energy reduction of 7.70% for winter and 16.57% for the summer season. The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Cleaner Production<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jclepro.2021.129172" target="_blank">https://dx.doi.org/10.1016/j.jclepro.2021.129172</a></p>2021-11-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jclepro.2021.129172https://figshare.com/articles/journal_contribution/Energy_utilization_assessment_of_a_semi-closed_greenhouse_using_data-driven_model_predictive_control/24339928CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/243399282021-11-15T00:00:00Z |
| spellingShingle | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control Farhat Mahmood (15468854) Agricultural, veterinary and food sciences Agriculture, land and farm management Food sciences Information and computing sciences Data management and data science Model predictive control Energy saving Greenhouse Agriculture Food |
| status_str | publishedVersion |
| title | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| title_full | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| title_fullStr | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| title_full_unstemmed | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| title_short | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| title_sort | Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control |
| topic | Agricultural, veterinary and food sciences Agriculture, land and farm management Food sciences Information and computing sciences Data management and data science Model predictive control Energy saving Greenhouse Agriculture Food |