An explainable & prescriptive solution for space-based energy consumption optimization using BIM data & genetic algorithm

• Published in: Journal of Building Engineering Vol. 92; p. 109763
• Main Authors: Kiavarz, Hamid, Jadidi, Mojgan, Rajabifard, Abbas, Sohn, Gunho
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024
• Subjects: BIM; Building energy consumption estimation; Explainable algorithm; Genetic algorithm; Prescriptive analysis; Building energy consumption estimation; Explainable algorithm; Prescriptive analysis; Genetic algorithm; BIM
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2024.109763

Creating energy-efficient buildings is a multifaceted challenge that involves carefully considering architectural, mechanical, and electrical parameters. With the increasing emphasis on low-energy building mandates and sustainable construction practices, integrating energy performance simulation into the design process has become imperative. However, existing methods often struggle to handle the vast amount of design information and to explore optimal alternatives effectively. While recent approaches such as Building Information Models (BIM) and data-driven solutions show promise for energy analysis, they frequently lack transparency and robust design optimization capabilities, limiting their applicability in critical contexts within the Architecture, Engineering, Construction, and Operation (AECO) industry. In response to these challenges, this research proposes a novel prescriptive model aimed at addressing these limitations. The proposed methodology integrates building energy simulation with optimization techniques, utilizing BIM data and a Genetic Algorithm (GA). The Genetic algorithm provides some optimized solutions, including different alternatives of geometry, material, electrical, and mechanical elements to help engineering for data-driven decision-making. This integration aims to develop a model that is both prescriptive and explainable for indoor building design. Leveraging the value engineering method, the proposed approach seeks to strike a balance between energy consumption, functionality, and cost. By doing so, it aims to enhance energy efficiency while providing tangible design optimization solutions. •Address the gap in design and retrofitting of energy-efficient buildings by integrating building energy simulation with optimization techniques, using Building Information Modeling (BIM) data and Genetic Algorithm (GA).•Develop a prescriptive and explainable model using Genetic Algorithm (GA) for indoor building design in room-based level, leveraging BIM interoperability to recommend optimal energy solutions.•Empover by value engineering method to balance energy consumption, functionality, and cost, providing engineers and designers with insights to optimize building energy performance effectively.•Offer compatibility of the well-known building 3D data model (BIM) with AI-inspired data-driven solutions to help AECO communities with informed decision-making is relevant to the spatial issue that is leading towards Construction5.0.