Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

• Published in: Buildings (Basel) Vol. 13; no. 12; p. 3084
• Main Authors: Bamdad, Keivan, Mohammadzadeh, Navid, Cholette, Michael, Perera, Srinath
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Air temperature; Algorithms; Ant colony optimization; Australia; Building automation; Building management systems; buildings; Case studies; Computational efficiency; Computer applications; Computing time; Controllers; Cooling; Cost control; Energy; Energy conservation; Energy consumption; Energy efficiency; energy savings; EnergyPlus; Green buildings; Heating; HVAC; HVAC equipment; Mathematical functions; model predictive control; Office buildings; Optimal control; Optimization; Paradigms; Parameter estimation; Parameter identification; Peak load; Physics; Predictive control; Simulation; Trajectory optimization; United Kingdom; Water temperature; white-box; Australia; United Kingdom
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings13123084

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.