Performance evaluation of an energy-efficient nonlinear model predictive controller for direct expansion systems

While model predictive control (MPC) has been extensively studied for energy-efficient indoor thermal environment control in small to medium-scale buildings, existing MPC approaches often prioritize temperature-only optimization, neglecting humidity dynamics and latent heat transfer, which limits energy efficiency and thermal comfort in humid climates. To address this gap, an energy-efficient nonlinear MPC framework for direct expansion (DX) air conditioning systems to simultaneously regulate indoor temperature and humidity is developed. A coupled building thermal-moisture dynamic model with empirical models of the DX system is established to facilitate the design of the nonlinear MPC. The framework employs a second-order moisture model to accurately capture moisture exchange dynamics, moving beyond simplistic insulated-zone assumptions prevalent in prior work. The performances of the proposed MPC for the DX system with different control settings and weather conditions are examined. Results demonstrate that the controller maintains the predicted mean vote (PMV) within the optimal comfort range (−0.2 to +0.2) while achieving significant energy savings. Up to 12.3% energy reduction compared to temperature-only MPC, when weighting humidity control (α = 0.5). Tight PMV distributions centered near neutral states, ensuring occupant comfort without compromising efficiency. This work bridges critical research gaps by advancing nonlinear, humidity-aware MPC for DX systems, offering a practical solution to enhance building energy efficiency in hot-humid regions.