Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control
Greenhouses in arid climates require advanced control systems to maintain the microclimate and reduce energy utilization, ensuring economic viability. To address these challenges, model predictive control is an effective method that forecasts the system’s future state and adjusts control variables a...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | Energy Conversion and Management: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525000716 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321700871634944 |
|---|---|
| author | Farhat Mahmood Rajesh Govindan Tareq Al-Ansari |
| author_facet | Farhat Mahmood Rajesh Govindan Tareq Al-Ansari |
| author_sort | Farhat Mahmood |
| collection | DOAJ |
| description | Greenhouses in arid climates require advanced control systems to maintain the microclimate and reduce energy utilization, ensuring economic viability. To address these challenges, model predictive control is an effective method that forecasts the system’s future state and adjusts control variables accordingly. However, deterministic model predictive control does not account for system uncertainties, leading to performance degradation. Therefore, this study proposes an improved model predictive control framework that utilizes an artificial neural network developed from historical greenhouse data. This method uses a double layer approach, where the primary controller provides the nominal trajectory, and an ancillary controller adjusts for uncertainties. The double layer predictive control framework was assessed under varying conditions to evaluate the performance in terms of temperature control and energy utilization. Results illustrated that, despite system uncertainties, the double layer model predictive control framework outperformed the existing greenhouse climate system, deterministic and robust model predictive control approaches. It demonstrated mean absolute errors of 0.09 °C in winter and 0.10 °C in summer, with corresponding root mean squared errors of 0.19 °C and 0.36 °C, respectively. Moreover, the double layer model predictive control method reduced energy utilization by 20.01 % in winter and 13.34 % in summer compared to the existing control system over a 4 d simulation period. |
| format | Article |
| id | doaj-art-565d68a0f4ea46b3b32fd4a671581214 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-565d68a0f4ea46b3b32fd4a6715812142025-08-20T03:49:41ZengElsevierEnergy Conversion and Management: X2590-17452025-04-012610093910.1016/j.ecmx.2025.100939Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive controlFarhat Mahmood0Rajesh Govindan1Tareq Al-Ansari2College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, QatarCollege of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, QatarCorresponding author.; College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, QatarGreenhouses in arid climates require advanced control systems to maintain the microclimate and reduce energy utilization, ensuring economic viability. To address these challenges, model predictive control is an effective method that forecasts the system’s future state and adjusts control variables accordingly. However, deterministic model predictive control does not account for system uncertainties, leading to performance degradation. Therefore, this study proposes an improved model predictive control framework that utilizes an artificial neural network developed from historical greenhouse data. This method uses a double layer approach, where the primary controller provides the nominal trajectory, and an ancillary controller adjusts for uncertainties. The double layer predictive control framework was assessed under varying conditions to evaluate the performance in terms of temperature control and energy utilization. Results illustrated that, despite system uncertainties, the double layer model predictive control framework outperformed the existing greenhouse climate system, deterministic and robust model predictive control approaches. It demonstrated mean absolute errors of 0.09 °C in winter and 0.10 °C in summer, with corresponding root mean squared errors of 0.19 °C and 0.36 °C, respectively. Moreover, the double layer model predictive control method reduced energy utilization by 20.01 % in winter and 13.34 % in summer compared to the existing control system over a 4 d simulation period.http://www.sciencedirect.com/science/article/pii/S2590174525000716Model predictive controlArtificial neural networkGreenhouse temperatureEnergy managementUncertainty |
| spellingShingle | Farhat Mahmood Rajesh Govindan Tareq Al-Ansari Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control Energy Conversion and Management: X Model predictive control Artificial neural network Greenhouse temperature Energy management Uncertainty |
| title | Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control |
| title_full | Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control |
| title_fullStr | Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control |
| title_full_unstemmed | Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control |
| title_short | Efficient energy management and temperature control of a high-tech greenhouse using an improved data-driven model predictive control |
| title_sort | efficient energy management and temperature control of a high tech greenhouse using an improved data driven model predictive control |
| topic | Model predictive control Artificial neural network Greenhouse temperature Energy management Uncertainty |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525000716 |
| work_keys_str_mv | AT farhatmahmood efficientenergymanagementandtemperaturecontrolofahightechgreenhouseusinganimproveddatadrivenmodelpredictivecontrol AT rajeshgovindan efficientenergymanagementandtemperaturecontrolofahightechgreenhouseusinganimproveddatadrivenmodelpredictivecontrol AT tareqalansari efficientenergymanagementandtemperaturecontrolofahightechgreenhouseusinganimproveddatadrivenmodelpredictivecontrol |