A Systematic Bibliometric Analysis of Energy Optimization Methods in Buildings

Problem statement: With increasing environmental concerns and the need to reduce energy consumption, the use of optimization methods for improving building performance has expanded. Since buildings are among the largest energy consumers and greenhouse gas emitters, enhancing their energy efficiency can significantly reduce pollutants and costs. This study examines various optimization methods, including Genetic Algorithms and Particle Swarm Optimization, to analyze trends and identify effective techniques for improving building energy performance. Which optimization methods play a more effective role in building energy simulation, and how are these methods distributed and utilized in Bibliometric research? Research objective: This study aims to identify and analyze widely used and effective optimization methods for improving building energy performance. The present study examines the distribution and frequency of these methods in Bibliometric articles and seeks to identify existing trends and the contribution of each approach to optimizing energy consumption and other aspects of building performance. Research method: Within the framework of a systematic review and to accurately identify optimization methods in building energy, a targeted search was conducted in reputable national and international databases using relevant keywords. After an initial screening and selection of related sources, data analysis was performed using VOS Viewer and bibliometric techniques to extract connections among Bibliometric texts. A conceptual model of effective optimization methods for improving building performance was developed, leading to a comprehensive understanding of their application and impact. Conclusion: Optimization methods, particularly Genetic Algorithms and Swarm Intelligence, are crucial in enhancing building energy performance. A comprehensive analysis of current trends underscores the necessity of integrating real-world data and intelligent techniques to develop more efficient solutions.