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

Ghezlane Halhoul Merabet, Mohamed Essaaidi+7 more|arXiv (Cornell University)|Apr 6, 2021

Building Energy and Comfort Optimization192 references4 citations

TL;DR

This systematic review evaluates AI-assisted techniques for intelligent building control systems, focusing on balancing thermal comfort and energy efficiency in HVAC systems. It identifies optimization, pattern recognition, and predictive control as key AI functions, highlighting the need for high-quality real-world data to improve performance, which remains suboptimal due to data limitations in the building sector.

ABSTRACT

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.

Motivation & Objective

• To analyze the state of the art in AI-based control systems for building HVAC operations.
• To identify the key AI techniques used for balancing thermal comfort and energy efficiency.
• To evaluate the performance and implementation of these AI techniques in real-world building applications.
• To highlight gaps in data quality and availability that hinder AI model effectiveness in building energy systems.

Proposed method

• Conducted a systematic literature review of AI-assisted building control systems from 2010 to 2021.
• Categorized AI techniques into functions such as pattern recognition, optimization, and predictive control.
• Evaluated 20 AI tools based on their outputs, implementation methods, and reported performance in energy and comfort metrics.
• Assessed the real-world applicability and data requirements of each AI-based solution.
• Synthesized findings on the strengths, limitations, and research gaps in current AI applications for building control.
• Used bibliometric analysis to map the evolution and focus areas of AI research in building energy systems.

Experimental results

Research questions

• RQ1 Which AI techniques are most commonly applied in intelligent building control systems for thermal comfort and energy efficiency?
• RQ2 How do AI-based control systems perform in real-world building environments compared to conventional methods?
• RQ3 What are the primary data-related challenges affecting the deployment and performance of AI models in building energy systems?
• RQ4 To what extent do AI techniques improve energy efficiency without compromising occupant thermal comfort?
• RQ5 What are the key research gaps and future directions in AI-driven building control, particularly regarding data quality and scalability?

Key findings

• Among the 20 AI tools reviewed, optimization and predictive control were the most frequently applied techniques for balancing energy use and thermal comfort.
• AI-based systems demonstrated potential for reducing energy consumption in HVAC systems, though performance varies significantly across studies.
• A major limitation identified was the lack of high-quality, real-world data required to train and validate AI models effectively.
• Pattern recognition and anomaly detection functions were used to improve system responsiveness and fault detection in building operations.
• Despite promising results, the performance of AI-based control systems remains unsatisfactory in many cases due to data scarcity and implementation complexity.
• The review underscores the need for standardized datasets and improved data collection infrastructure in the building and energy sectors.