Mahmood Shahid, Sun Huaping, El-Kenawy El-Sayed M, Iqbal Asifa, Alharbi Amal H, Khafaga Doaa Sami
School of Finance and Economics, Jiangsu University, Zhenjiang, China.
School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China.
Sci Rep. 2024 Sep 2;14(1):20331. doi: 10.1038/s41598-024-70519-y.
A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building's life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.
绿色建筑（GB）是一种在建筑的整个生命周期中整合环保技术和可持续程序的设计理念。然而，由于不同的绿色要求和性能被整合到建筑设计中，绿色建筑的设计过程通常比传统建筑耗时更长。机器学习（ML）和其他先进的人工智能（AI），如深度学习（DL）技术，经常被用来帮助设计师更快、更精确地完成工作。因此，本研究旨在利用ML和DL技术开发一个绿色建筑设计预测模型，以优化资源消耗、提高居住者舒适度，并减少绿色建筑设计过程中建筑环境对环境的影响。一个名为ASHARE-884的数据集被应用于所提出的模型。应用了探索性数据分析（EDA），包括清理、排序，并利用标签编码将类别数据转换为数值。在数据预处理中，应用Z-Score归一化技术对数据进行归一化。经过数据分析和预处理后，预处理后的数据被用作机器学习（ML）的输入，如随机森林（RF）、决策树（DT）和极限梯度提升（Extreme GB），以及深度学习（DL）的输入，如用于绿色建筑设计的图神经网络（GNN）、长短期记忆网络（LSTM）和循环神经网络（RNN）技术，通过解决绿色建筑设计过程的不同标准来提高环境可持续性。使用不同的评估指标，如准确率、精确率、召回率和F1分数，来评估所提出模型的性能。实验结果表明，所提出的GNN和LSTM模型在绿色建筑环境可持续性方面比传统的DL技术更准确、更高效地发挥作用。
