Citation: Suphavarophas, P.; Wongmahasiri, R.; Keonil, N.; Bunyarittikit, S. A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings. Buildings 2024, 14, 1311. https://doi.org/10.3390/ buildings14051311 Academic Editors: Wil Ward and Hadi Arbabi Received: 26 March 2024 Revised: 25 April 2024 Accepted: 1 May 2024 Published: 7 May 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). buildings Review A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings Phattranis Suphavarophas , Rungroj Wongmahasiri *, Nuchnapang Keonil and Suphat Bunyarittikit School of Architecture, Art and Design, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand; 65026022@kmitl.ac.th (P.S.); nuchnapang.ke@kmitl.ac.th (N.K.); suphat.bu@kmitl.ac.th (S.B.) * Correspondence: rungroj.wo@kmitl.ac.th; Tel.: +66-812518072 Abstract: Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but signifi- cant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodol- ogy in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention. Keywords: generative design; generative method; energy efficient; buildings 1. Introduction Currently, we are confronted with an escalating energy crisis and the intensifying impacts of climate change, both of which pose increasingly severe repercussions on a global scale with each passing day [1,2]. Buildings are a primary contributor, accounting for ap- proximately one-third of global greenhouse gas emissions [3,4]. The building sector is also a significant source of carbon dioxide (CO 2 ) emissions, releasing a substantial quantity into the environment [5]. The energy consumption attributed to buildings comprises one-third of the world’s total energy consumption [6]. The most significant portion of building energy consumption is allocated to Heating, Ventilation, and Air Conditioning (HVAC) systems, accounting for 40% of total building energy usage [7–9]. Consequently, architectural design should prioritize enhancing energy efficiency within buildings [2,4,7,10,11] to make energy usage more cost-effective and reduce carbon emissions, a crucial aspect of mitigating the environmental impact. Many studies have indicated that early design stages are crucial for achieving energy efficiency in buildings [12–14]. Multiple objectives influence the overall performance of a design [15]. Therefore, energy assessment is of paramount importance [16,17]. Genera- tive design has been developed to enhance efficiency by facilitating evaluation from the earlier stages of the design process [18–20]. Generative design is an iterative process that automatically generates all design options based on specified conditions [21,22]. Genera- tive design enhances efficiency through existing processes by employing computational Buildings 2024, 14, 1311. https://doi.org/10.3390/buildings14051311 https://www.mdpi.com/journal/buildings