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Building operations represent a significant percentage of the total primaryenergy consumed in most countries due to the proliferation of Heating,Ventilation and Air-Conditioning (HVAC) installations in response to thegrowing demand for improved thermal comfort. Reducing the associated energyconsumption while maintaining comfortable conditions in buildings areconflicting objectives and represent a typical optimization problem thatrequires intelligent system design. Over the last decade, differentmethodologies based on the Artificial Intelligence (AI) techniques have beendeployed to find the sweet spot between energy use in HVAC systems and suitableindoor comfort levels to the occupants. This paper performs a comprehensive andan in-depth systematic review of AI-based techniques used for building controlsystems by assessing the outputs of these techniques, and their implementationsin the reviewed works, as well as investigating their abilities to improve theenergy-efficiency, while maintaining thermal comfort conditions. This enables aholistic view of (1) the complexities of delivering thermal comfort to usersinside buildings in an energy-efficient way, and (2) the associatedbibliographic material to assist researchers and experts in the field intackling such a challenge. Among the 20 AI tools developed for both energyconsumption and comfort control, functions such as identification andrecognition patterns, optimization, predictive control. Based on the findingsof this work, the application of AI technology in building control is apromising area of research and still an ongoing, i.e., the performance ofAI-based control is not yet completely satisfactory. This is mainly due in partto the fact that these algorithms usually need a large amount of high-qualityreal-world data, which is lacking in the building or, more precisely, theenergy sector.

Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques