Energy, thermal comfort, and indoor air quality: Multi-objective optimization review

<p>The reliance on optimization techniques for robust assessments of environmental and energy-saving solutions has been largely driven by the increasing need to comply with international energy policies. However, numerous challenges arise from inherently conflicting objectives for a <a href="https://www.sciencedirect.com/topics/engineering/sustainable-build-environment" target="_blank">sustainable built environment</a>, that is, maximizing thermal comfort, and <a href="https://www.sciencedirect.com/topics/engineering/indoor-air" target="_blank">indoor air</a> quality, while minimizing energy consumption, forming a multi-objective optimization problem. Consequently, studies seeking multi-faceted <a href="https://www.sciencedirect.com/topics/engineering/optimality" target="_blank">optimality</a> in the design and/or operation of low-energy buildings have exponentially increased over the past few years. This research critically reviews the latest multi-objective optimization studies that present energy consumption, thermal comfort, and indoor air quality as competing targets. By examining 82 records between 2013 and 2022, key discussions focused on commonly investigated objective functions, design variables, and performance metrics. The review also investigates the latest research trends, optimization techniques, algorithms, and tools, and identifies gaps in knowledge and potential future research directions. The review results showed that most studies used a holistic approach that targeted all three objective functions, with the largest portion performed on office and residential buildings. The most commonly investigated design variables are system-related variables, whereas building-related and occupant-related variables are often overlooked. Coupling simulation tools and optimization algorithms is the most widely utilized optimization approach, with <a href="https://www.sciencedirect.com/topics/engineering/genetic-algorithm" target="_blank">genetic algorithms</a> being the most employed. These findings suggest a promising area for future research on methodological optimization approaches, which are expected to be significantly transformed with the rapid development of artificial intelligence technologies. </p>