Model Predictive Control for Smart HVAC Systems in Green Buildings

This study investigates the application of Model Predictive Control (MPC) in optimizing smart Heating, Ventilation, and Air Conditioning (HVAC) systems for green buildings. MPC algorithms are designed to predict future indoor conditions and optimize energy usage by adjusting system outputs proactively. A simulated green building environment was used for evaluation, including real-time weather data and occupancy-based control inputs. The performance of MPC was benchmarked against traditional Proportional-Integral-Derivative (PID) control systems in terms of energy efficiency, temperature stability, and operational cost. Results indicated that MPC achieved a 28.7% reduction in energy consumption, improved thermal comfort, and lowered peak load demands. Statistical analysis and regression models confirmed significant differences in performance metrics. These findings demonstrate the viability of MPC in advancing energy-efficient HVAC operations and support its integration in next-generation smart building infrastructures. Keywords: Model Predictive Control, Smart HVAC, Green Buildings, Energy Efficiency, Predictive Modeling, Building Automation