A LIME-LSTSNM approach based green building sustainability prediction using BIM design

• Published in: Sustainable computing informatics and systems Vol. 47; p. 101155
• Main Authors: Xia, Yan, Li, Yaning, Liu, Siqin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: BIM; Green building design; Long Short-Term Skip Norm Memory (LSTSNM) and K-Means Algorithm (KMA); Multiresolution Kalman Filter (MKF); Penalized K-Log Euclidean Neighbour (PKLEN); Snow Ablation Avalanching Optimizer (SA2O); Penalized K-Log Euclidean Neighbour (PKLEN); Multiresolution Kalman Filter (MKF); Green building design; Long Short-Term Skip Norm Memory (LSTSNM) and K-Means Algorithm (KMA); Snow Ablation Avalanching Optimizer (SA2O); BIM
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2025.101155

This research presents a climate change-based parameter optimisation approach for sustainable green building design. The process begins with a Building Information Modeling (BIM)-based design, followed by a Design-Builder simulation. Climatic data is collected and pre-processed, and building parameters are optimized using SA2O, considering this data. BIM-based building parameters and the optimized data are then extracted. The simulation output, along with sensor and historical data, are fused using the Multiresolution Kalman Filter (MKF) technique. Incomplete data is handled with Penalized K-Log Euclidean Neighbor (PKLEN), followed by season-based grouping using KMA. Non-linear dynamics are analyzed, and features are extracted from both the grouped and non-linear data. The sustainability factor is predicted using Local Interpretable Model-agnostic Explanations (LIME), with Long Short-Term Skip Norm Memory (LSTSNM), and feedback is provided to optimise the building parameters for sustainable green building design. Experimental results show that this model achieved an accuracy of 98.24 %, demonstrating the effectiveness of the proposed approach in enhancing sustainability in building design while considering climate change. •The paper introduces a climatic change-based parameter optimization method for sustainable green building design using BIM and SA2O.•It utilizes data fusion through MKF and handles incomplete data with PKLEN to ensure accuracy in the building parameters.•The model applies KMA for season-based grouping and analyses non-linear dynamics to extract relevant features.