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Comparative analysis on predictability of natural ventilation rate based on machine learning algorithms
Building and Environment ( IF 7.4 ) Pub Date : 2021-02-24 , DOI: 10.1016/j.buildenv.2021.107744
Hansaem Park , Dong Yoon Park

The demand for efficient natural ventilation (NV) systems has increased for the development of sustainable buildings. However, the uncertainty of NV remains a challenging issue for appropriate utilization strategies of NV. For the successful implementation of NV systems in buildings, it is essential to clarify when and how to use NV systems in advance. In order to achieve the objectives, this study investigated the predictive models of NV rate (NVR) through eight machine learning (ML) algorithms, which are suitable for the interpretation of non-linear relationships between the measured indoor and outdoor environmental variables. Among all of the algorithms, deep neural network (DNN) ensured the best prediction performance for the NVR and it was shown that 40%, 46%, and 38% better predictive performance in terms of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) than multivariate linear regression (MLR), which had the highest error rate, respectively. Based on the Shapley additive explanation (SHAP), the most influential features that affected to results of predictive models were examined and most of the ML approaches, except for MLR, had similar features (the pressure difference, outdoor temperature, wind speed, indoor relative humidity, solar radiation, the difference of indoor/outdoor relative humidity, and wind direction). The results of this study can improve the prediction performance for NVR, and this would contribute to the development of an intelligent NV system. Future work needs to develop the optimal operating strategies for hybrid ventilation systems integrating NV and mechanical systems.



中文翻译:

基于机器学习算法的自然通风率可预测性比较分析

对于可持续建筑的发展,对有效自然通风(NV)系统的需求已经增加。但是,NV的不确定性对于NV的适当利用策略仍然是一个具有挑战性的问题。为了在建筑物中成功实施NV系统,必须事先弄清楚何时以及如何使用NV系统。为了达到目标,本研究通过八种机器学习(ML)算法研究了NV率(NVR)的预测模型,这些模型适合于解释所测量的室内和室外环境变量之间的非线性关系。在所有算法中,深度神经网络(DNN)确保了NVR的最佳预测性能,并且在平均绝对误差(MAE)方面,预测性能提高了40%,46%和38%,均方根误差(RMSE)和绝对绝对误差(MAPE)高于误差率最高的多元线性回归(MLR)。根据Shapley加性解释(SHAP),检查了影响预测模型结果的最有影响力的特征,除MLR外,大多数ML方法具有相似的特征(压力差,室外温度,风速,室内相对湿度)。湿度,太阳辐射,室内/室外相对湿度的差异以及风向)。这项研究的结果可以提高NVR的预测性能,这将有助于智能NV系统的开发。未来的工作需要为结合了NV和机械系统的混合通风系统开发最佳的操作策略。与平均绝对百分比误差(MAPE)相比,多元线性回归(MLR)的误差率最高。根据Shapley加性解释(SHAP),检查了影响预测模型结果的最有影响力的特征,除MLR外,大多数ML方法具有相似的特征(压力差,室外温度,风速,室内相对湿度)。湿度,太阳辐射,室内/室外相对湿度的差异以及风向)。这项研究的结果可以提高NVR的预测性能,这将有助于智能NV系统的开发。未来的工作需要为结合了NV和机械系统的混合通风系统开发最佳的操作策略。与平均绝对百分比误差(MAPE)相比,多元线性回归(MLR)的误差率最高。根据Shapley加性解释(SHAP),检查了影响预测模型结果的最有影响力的特征,除MLR外,大多数ML方法具有相似的特征(压力差,室外温度,风速,室内相对湿度)。湿度,太阳辐射,室内/室外相对湿度的差异以及风向)。这项研究的结果可以提高NVR的预测性能,这将有助于智能NV系统的开发。未来的工作需要为结合了NV和机械系统的混合通风系统开发最佳的操作策略。

更新日期:2021-03-11
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