Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm
The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/12/3084 |
| _version_ | 1797381741779877888 |
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| author | Keivan Bamdad Navid Mohammadzadeh Michael Cholette Srinath Perera |
| author_facet | Keivan Bamdad Navid Mohammadzadeh Michael Cholette Srinath Perera |
| author_sort | Keivan Bamdad |
| collection | DOAJ |
| description | The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm. |
| first_indexed | 2024-03-08T20:55:49Z |
| format | Article |
| id | doaj.art-7c8100b4ebf3471fa5256f4bc1700ccd |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-08T20:55:49Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-7c8100b4ebf3471fa5256f4bc1700ccd2023-12-22T13:58:25ZengMDPI AGBuildings2075-53092023-12-011312308410.3390/buildings13123084Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO AlgorithmKeivan Bamdad0Navid Mohammadzadeh1Michael Cholette2Srinath Perera3School of Engineering, Design and Built Environment, Western Sydney University, Sydney 2116, AustraliaSchool of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane 2000, AustraliaSchool of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane 2000, AustraliaSchool of Engineering, Design and Built Environment, Western Sydney University, Sydney 2116, AustraliaThe deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.https://www.mdpi.com/2075-5309/13/12/3084model predictive controlEnergyPlusenergy savingswhite-boxHVACbuildings |
| spellingShingle | Keivan Bamdad Navid Mohammadzadeh Michael Cholette Srinath Perera Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm Buildings model predictive control EnergyPlus energy savings white-box HVAC buildings |
| title | Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm |
| title_full | Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm |
| title_fullStr | Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm |
| title_full_unstemmed | Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm |
| title_short | Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm |
| title_sort | model predictive control for energy optimization of hvac systems using energyplus and aco algorithm |
| topic | model predictive control EnergyPlus energy savings white-box HVAC buildings |
| url | https://www.mdpi.com/2075-5309/13/12/3084 |
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