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Predicting occupancy counts using physical and statistical Co-2-based modeling methodologies

机译：使用基于物理和统计基于Co-2的建模方法预测入住人数

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Energy consumption and indoor environment quality (IEQ) of buildings have been linked to human occupants. Predicting the number of occupants in a space is essential for the effective management of various building operation functions as well as improve energy efficiency. This study is the first to compare the performance of physical and statistical models in predicting occupant counts in a high volume lecture theatre (Occ = 200) using CO2 sensors. CO2 measurements and actual occupant numbers were obtained for 4 months to provide robust data comparison of the methodologies. It was found that that the dynamic physical models and Support Vector Machines (SVM) and Artificial Neural Networks (ANN) models utilizing a combination of average and first order differential CO2 concentrations performed the best in terms of predicting occupancy counts with the ANN and SVM models showing higher predictive performance. RMSE values for the corresponding models were 12.8, 12.6 and 12.1 respectively and correlation coefficients were all greater than 0.95. The relatively good agreement between dynamic physical model predictions and ground truth shows that the dynamic mass balanced model is adequate for predicting occupancy counts provided that the air exchange rates measured are accurate. Model average accuracies across all tolerance was between 70 and 76% demonstrating good performance for a large number of occupants. A discussion on the merits and limitations of each model types was presented to provide guidance on the adoption of various models. (C) 2017 Elsevier Ltd. All rights reserved.
机译：建筑物的能耗和室内环境质量（IEQ）与人类居住有关。预测空间中的居住者数量对于有效管理各种建筑物运营功能以及提高能源效率至关重要。这项研究是第一个比较物理模型和统计模型在预测使用CO2传感器的大教室（Occ = 200）中的人数时的性能的方法。获得了4个月的CO2测量值和实际乘员数，以提供对方法进行可靠的数据比较。结果发现，动态平均模型和支持向量机（SVM）和人工神经网络（ANN）模型结合了平均和一阶差分CO2浓度，在预测ANN和SVM模型的占用率方面表现最佳显示更高的预测性能。相应模型的RMSE值分别为12.8、12.6和12.1，相关系数均大于0.95。动态物理模型预测与地面真实情况之间的相对较好的一致性表明，只要测量的空气交换率准确，动态质量平衡模型就足以预测占用人数。所有容忍度的模型平均准确度在70％到76％之间，这表明大量乘员的表现良好。讨论了每种模型类型的优缺点，以为采用各种模型提供指导。 （C）2017 Elsevier Ltd.保留所有权利。

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