OPTIMIZATION OF HVAC ENERGY CONSUMPTION UNDER UNCERTAINTY OF EXTERNAL CONDITIONS: A PROBABILISTIC MODELING APPROACH

Heating, Ventilation, and Air Conditioning (HVAC) systems constitute a dominant share of building energy consumption, accounting for approximately 40% of total energy use. The inherent uncertainty in external conditions, including weather variability and stochastic occupancy patterns, significantly affects both energy efficiency and indoor environmental quality. Deterministic control strategies are limited in their ability to address these uncertainties, which often results in suboptimal performance and increased operational costs. This study develops a probabilistic optimization framework for HVAC energy management that explicitly incorporates uncertainty in external inputs. The proposed approach models occupancy as a stochastic process and represents weather conditions using probabilistic distributions. Indoor dynamics of temperature and CO₂ concentration are described by stochastic differential equations, which are integrated into a constrained optimization problem. The objective function minimizes the expected value of total energy consumption subject to probabilistic comfort constraints.