  Building operations represent a significant percentage of the total primary
energy consumed in most countries due to the proliferation of Heating,
Ventilation and Air-Conditioning (HVAC) installations in response to the
growing demand for improved thermal comfort. Reducing the associated energy
consumption while maintaining comfortable conditions in buildings are
conflicting objectives and represent a typical optimization problem that
requires intelligent system design. Over the last decade, different
methodologies based on the Artificial Intelligence (AI) techniques have been
deployed to find the sweet spot between energy use in HVAC systems and suitable
indoor comfort levels to the occupants. This paper performs a comprehensive and
an in-depth systematic review of AI-based techniques used for building control
systems by assessing the outputs of these techniques, and their implementations
in the reviewed works, as well as investigating their abilities to improve the
energy-efficiency, while maintaining thermal comfort conditions. This enables a
holistic view of (1) the complexities of delivering thermal comfort to users
inside buildings in an energy-efficient way, and (2) the associated
bibliographic material to assist researchers and experts in the field in
tackling such a challenge. Among the 20 AI tools developed for both energy
consumption and comfort control, functions such as identification and
recognition patterns, optimization, predictive control. Based on the findings
of this work, the application of AI technology in building control is a
promising area of research and still an ongoing, i.e., the performance of
AI-based control is not yet completely satisfactory. This is mainly due in part
to the fact that these algorithms usually need a large amount of high-quality
real-world data, which is lacking in the building or, more precisely, the
energy sector.
