ABSTRACT

Thanks to the recent advances in computer technology, many building energy performance simulation tools have been developed in the current market. Designers and architects are interested in working on this topic in the early phases of the project. However, effective energy solutions are computationally expensive. As a result, having a comprehensive insight into the project conditions in the early phases of the work is a vital issue. The present study aimed to propose an artificial intelligence (Al) model to generate a reasonably accurate estimate in a short time. To this end, four machine learning models and one artificial neural network (ANN) are selected and their results are compared to assess their capabilities in energy performance estimation. This study investigates the influence of the exterior louver design on the interior energy performance of a structure. A specific dataset is generated and tested on four powerful regression models (i.e., polynomial Linear Regression, Random Forests (RF), Decision Tree (DT), and Support Vector Regression (SVR)) and one Artificial Neural Network (ANN). Finally, a comparative analysis is presented. The findings of this research support the use of machine learning tools and ANNs as a convenient and accurate strategy for predicting building parameters.

摘要

由于计算机技术的最新发展，在当前市场上已经开发了许多建筑能效模拟工具。设计师和建筑师对项目早期阶段的这个主题感兴趣。然而，有效的能量解决方案在计算上是昂贵的。因此，在工作的早期阶段全面了解项目条件是至关重要的问题。本研究旨在提出一种人工智能（Al）模型，以在短时间内生成合理准确的估算值。为此，选择了四个机器学习模型和一个人工神经网络（ANN），并对其结果进行比较，以评估其在能源绩效评估中的能力。这项研究调查了外部百叶窗设计对结构内部能量性能的影响。在四个强大的回归模型（即多项式线性回归，随机森林（RF），决策树（DT）和支持向量回归（SVR））和一个人工神经网络（ANN）上生成并测试了一个特定的数据集。最后，进行了比较分析。这项研究的发现支持使用机器学习工具和人工神经网络作为预测建筑物参数的便捷而准确的策略。