Ensuring optimal Indoor Environmental Quality (IEQ) is vital for occupant health and productivity, yet it often comes at a high energy cost in conventional Heating, Ventilation, and Air Conditioning (HVAC) systems. This paper proposes a deep learning driven approach to proactively manage IEQ parameters specifically CO2 concentration, temperature, and humidity while balancing building energy efficiency. Leveraging the ROBOD dataset collected from a net-zero energy academic building, we benchmark three architectures--Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and a hybrid Convolutional Neural Network LSTM (CNN-LSTM)--to forecast IEQ variables across various time horizons. Our results show that GRU achieves the best short-term prediction accuracy with lower computational overhead, whereas CNN-LSTM excels in extracting dominant features for extended forecasting windows. Meanwhile, LSTM offers robust long-range temporal modeling. The comparative analysis highlights that prediction reliability depends on data resolution, sensor placement, and fluctuating occupancy conditions. These findings provide actionable insights for intelligent Building Management Systems (BMS) to implement predictive HVAC control, thereby reducing energy consumption and enhancing occupant comfort in real-world building operations.

翻译：确保最优的室内环境质量对于居住者的健康与工作效率至关重要，但在传统的供暖、通风与空调系统中，这往往伴随着高昂的能源成本。本文提出一种由深度学习驱动的方法，旨在主动管理室内环境质量参数，特别是二氧化碳浓度、温度和湿度，同时兼顾建筑能效。利用从一座净零能耗学术建筑收集的ROBOD数据集，我们对三种架构——长短期记忆网络、门控循环单元以及混合卷积神经网络-长短期记忆网络——进行了基准测试，以预测不同时间跨度下的室内环境质量变量。我们的结果表明，门控循环单元以较低的计算开销实现了最佳的短期预测精度，而混合卷积神经网络-长短期记忆网络在提取用于扩展预测窗口的主导特征方面表现优异。同时，长短期记忆网络提供了稳健的长程时间建模能力。对比分析表明，预测的可靠性取决于数据分辨率、传感器布设以及波动的占用情况。这些发现为智能建筑管理系统实施预测性供暖、通风与空调控制提供了可行的见解，从而在实际建筑运营中降低能耗并提升居住者舒适度。