A Review of Energy-Efficient HVAC (Heating, Ventilation, and Air Conditioning) Systems for Smart Buildings

Abstract

Effective HVAC (heating, ventilation, and air conditioning) systems management in buildings is necessary to attain energy saving and comfort. Adequate sophisticated control techniques that can adjust to changing environmental circumstances and occupant preferences are crucial for effectively balancing these goals. This paper discusses passive, active, and intelligent HVAC technologies, their advantages in energy conservation, indoor air quality, sustainability, and system reliability. A discussion on HVAC operations and popular systems type is provided in order to have background knowledge. An in-depth review of the literature will be done concerning recent machine learning and predictive control methods in optimization of HVAC, such as occupancy-based control, model predictive control, and gradient-based learning with a particular interest in energy savings and comfort of the occupants. Although progress is being made, the available literature tends to work with small scale or linear models and this restricts their usability to complicated and dynamic settings. The novelty of the research is that the multi-factor considerations, including the occupancy patterns, environmental uncertainties, renewable energy integration, and real-time adaptive control are put in a single framework. The primary contribution is that it suggests scalable and smart HVAC optimization that improves the energy use, cuts expenses, and guarantees the occupants comfort, which could not be done by linear or small-scale optimizations.