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

Author

Listed:
  • Abdul Mateen Khan

    (Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS Bandar, Seri Iskandar 32610, Perak, Malaysia
    Department of Civil Engineering, International Islamic University, Islamabad 44000, Pakistan)

  • 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)

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.

Suggested Citation

  • Abdul Mateen Khan & Muhammad Abubakar Tariq & Sardar Kashif Ur Rehman & Talha Saeed & Fahad K. Alqahtani & Mohamed Sherif, 2024. "BIM Integration with XAI Using LIME and MOO for Automated Green Building Energy Performance Analysis," Energies, MDPI, vol. 17(13), pages 1-36, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3295-:d:1429224
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    References listed on IDEAS

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