BIM Integration with XAI Using LIME and MOO for Automated Green Building Energy Performance Analysis

Abdul Mateen Khan (), Muhammad Abubakar Tariq, Sardar Kashif Ur Rehman, Talha Saeed, Fahad K. Alqahtani and Mohamed Sherif
Additional contact information
Abdul Mateen Khan: Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS Bandar, Seri Iskandar 32610, Perak, Malaysia
Muhammad Abubakar Tariq: Department of Civil Engineering, International Islamic University, Islamabad 44000, Pakistan
Sardar Kashif Ur Rehman: Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan
Talha Saeed: Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan
Fahad K. Alqahtani: Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Mohamed Sherif: Civil and Environmental Engineering Department, College of Engineering, University of Hawai’i at Manoa, Honolulu, HI 96822, USA

Energies, 2024, vol. 17, issue 13, 1-36

Abstract: Achieving sustainable green building design is essential to reducing our environmental impact and enhancing energy efficiency. Traditional methods often depend heavily on expert knowledge and subjective decisions, posing significant challenges. This research addresses these issues by introducing an innovative framework that integrates building information modeling (BIM), explainable artificial intelligence (AI), and multi-objective optimization. The framework includes three main components: data generation through DesignBuilder simulation, a BO-LGBM (Bayesian optimization–LightGBM) predictive model with LIME (Local Interpretable Model-agnostic Explanations) for energy prediction and interpretation, and the multi-objective optimization technique AGE-MOEA to address uncertainties. A case study demonstrates the framework’s effectiveness, with the BO-LGBM model achieving high prediction accuracy (R-squared > 93.4%, MAPE < 2.13%) and LIME identifying significant HVAC system features. The AGE-MOEA optimization resulted in a 13.43% improvement in energy consumption, CO 2 emissions, and thermal comfort, with an additional 4.0% optimization gain when incorporating uncertainties. This study enhances the transparency of machine learning predictions and efficiently identifies optimal passive and active design solutions, contributing significantly to sustainable construction practices. Future research should focus on validating its real-world applicability, assessing its generalizability across various building types, and integrating generative design capabilities for automated optimization.

Keywords: sustainable architecture; predictive modeling; energy optimization; building information modeling (BIM); explainable AI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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