A comprehensive review of building energy optimization using metaheuristic algorithms

Abstract

This review paper investigates the progression of building energy optimization (BEO), with particular emphasis on metaheuristic algorithms (MAs) within this field. This review emphasizes the need for energy-efficient buildings to reduce carbon footprints in response to global warming and the goals of the Paris Agreement. The paper outlines the scope and goals, aiming to deliver a comprehensive analysis of MAs and their applications in BEO. The introductory sections provide a foundational understanding of BEO methods, comparing traditional approaches, like linear and mixed-integer linear programming, with modern optimization techniques. The shortcomings of traditional methods in handling complex, real-world challenges are emphasized, leading to a thorough examination of Memetic Algorithms (MAs). These algorithms, noted for their flexibility, adaptability, and efficiency, are explored in-depth, along with various classifications. The benefits of MAs in solving complex optimization issues in BEO are highlighted, showcasing their superiority over classical approaches. The MAs application and common objective functions in BEO are presented. Also, the paper reviews in-depth the optimization techniques applied for simple and detailed office buildings, summarizing and comparing the findings to show practical results and methodologies. Further, the discussion extends to the challenges and limitations that have to be faced while applying the MAs. In conclusion, the main findings and final insights are summarized, emphasizing the effectiveness of these algorithms for efficient performance in BEO. This review is a helpful resource for both academics and practitioners, offering an overview of the current state and future potential of MAs for optimizing energy efficiency in buildings.