Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings
Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) met...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/12/3246 |
| _version_ | 1797564389688082432 |
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| author | Anass Berouine Radouane Ouladsine Mohamed Bakhouya Mohamed Essaaidi |
| author_facet | Anass Berouine Radouane Ouladsine Mohamed Bakhouya Mohamed Essaaidi |
| author_sort | Anass Berouine |
| collection | DOAJ |
| description | Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement. |
| first_indexed | 2024-03-10T18:57:36Z |
| format | Article |
| id | doaj.art-bc0267bf2e264e2db638066020a0f050 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:57:36Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bc0267bf2e264e2db638066020a0f0502023-11-20T04:41:27ZengMDPI AGEnergies1996-10732020-06-011312324610.3390/en13123246Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient BuildingsAnass Berouine0Radouane Ouladsine1Mohamed Bakhouya2Mohamed Essaaidi3College of Engineering and Architecture, International University of Rabat, LERMA Lab, Sala El Jadida 11100, MoroccoCollege of Engineering and Architecture, International University of Rabat, LERMA Lab, Sala El Jadida 11100, MoroccoCollege of Engineering and Architecture, International University of Rabat, LERMA Lab, Sala El Jadida 11100, MoroccoENSIAS, Mohamed V University, Rabat 10713, MoroccoVentilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.https://www.mdpi.com/1996-1073/13/12/3246energy efficiency in buildingsindoor air quality comfortCO<sub>2</sub> regulationventilation systems controlmodel and generalized predictive control |
| spellingShingle | Anass Berouine Radouane Ouladsine Mohamed Bakhouya Mohamed Essaaidi Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings Energies energy efficiency in buildings indoor air quality comfort CO<sub>2</sub> regulation ventilation systems control model and generalized predictive control |
| title | Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings |
| title_full | Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings |
| title_fullStr | Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings |
| title_full_unstemmed | Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings |
| title_short | Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings |
| title_sort | towards a real time predictive management approach of indoor air quality in energy efficient buildings |
| topic | energy efficiency in buildings indoor air quality comfort CO<sub>2</sub> regulation ventilation systems control model and generalized predictive control |
| url | https://www.mdpi.com/1996-1073/13/12/3246 |
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