A Comparative Study of Artificial Intelligence Models for Predicting Interior Illuminance
A Comparative Study of Artificial Intelligence Models for Predicting Interior Illuminance
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DOI：
10.1080/08839514.2021.1882794
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发表时间：
2021-02-14
影响因子：
2.8
通讯作者：
Arbab, Mojgan
中科院分区：
文献类型：
--
作者：
Arbab, Maryam;Rahbar, Morteza;Arbab, Mojgan
由于计算机技术的进步，目前市场上已经开发了许多建筑能源性能模拟工具。在项目的早期阶段，设计师和架构师对这个主题很感兴趣。然而，有效的能源解决方案在计算上是昂贵的。因此，在工作的早期阶段对项目条件有一个全面的了解是至关重要的。本研究旨在提出一种人工智能（Al）模型，以在短时间内产生合理准确的估计。为此，选择了四种机器学习模型和一种人工神经网络（ANN），并对它们的结果进行了比较，以评估它们在能源性能估计方面的能力。本研究探讨外百叶设计对结构内部能源性能的影响。生成一个特定的数据集，并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）上进行测试。最后，进行了比较分析。这项研究的结果支持使用机器学习工具和人工神经网络作为预测建筑参数的方便和准确的策略。
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.