Abstract Modern buildings are expected to fulfill energy efficiency regulations while providing a healthy and comfortable living environment for their occupants. Demand-controlled heating, ventilation, and air conditioning can improve energy efficiency and well-being, especially if changes in indoor environmental factors can be forecast reliably enough. As a first step, this study afforded a baseline for CO2 forecasting evaluation by providing for the public use a comprehensive dataset covering a full year. Secondly, we studied the applicability of four machine learning methods, Ridge regression, Decision Tree, Random Forest, and Multilayer Perceptron, for modeling the future concentration of CO2 in indoors. We evaluated their prediction accuracy within different forecasting and history window time frames and the impact of multiple sensor modalities. All models performed better than the baseline method of predicting the last observed value, and the Decision Tree was found to be almost as accurate as the computationally heavier Random Forest model. When the future forecasting window was longer than a minute, the optimal sensor modalities included occupant activity data from passive infrared sensors in addition to CO2 concentration. Our findings suggest that machine learning can be applied in multimodal time-series data to find a simple, accurate, and resource-efficient forecasting model for proactive control of indoor environments.

中文翻译：

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使用机器学习和一年数据集预测办公室室内二氧化碳浓度

摘要 现代建筑在为居住者提供健康、舒适的居住环境的同时，还应满足能效法规的要求。需求控制的供暖、通风和空调可以提高能源效率和幸福感，特别是如果可以足够可靠地预测室内环境因素的变化。作为第一步，这项研究通过为公众提供涵盖全年的综合数据集，为二氧化碳预测评估提供了基线。其次，我们研究了四种机器学习方法（岭回归、决策树、随机森林和多层感知器）对未来室内 CO2 浓度建模的适用性。我们评估了它们在不同预测和历史窗口时间范围内的预测准确性以及多种传感器模式的影响。所有模型的性能都优于预测最后观察值的基线方法，并且发现决策树几乎与计算量较大的随机森林模型一样准确。当未来的预测窗口超过一分钟时，最佳传感器模式除了 CO2 浓度外，还包括来自被动红外传感器的乘员活动数据。我们的研究结果表明，机器学习可以应用于多模态时间序列数据，以找到一种简单、准确且资源高效的预测模型，用于主动控制室内环境。并且发现决策树几乎与计算量更大的随机森林模型一样准确。当未来的预测窗口超过一分钟时，最佳传感器模式除了 CO2 浓度外，还包括来自被动红外传感器的乘员活动数据。我们的研究结果表明，机器学习可以应用于多模态时间序列数据，以找到一种简单、准确且资源高效的预测模型，用于主动控制室内环境。并且发现决策树几乎与计算量更大的随机森林模型一样准确。当未来的预测窗口超过一分钟时，最佳传感器模式除了 CO2 浓度外，还包括来自被动红外传感器的乘员活动数据。我们的研究结果表明，机器学习可以应用于多模态时间序列数据，以找到一种简单、准确且资源高效的预测模型，用于主动控制室内环境。