用深度学习预测室内环境，节能又舒适
Optimizing Indoor Environmental Quality in Smart Buildings Using Deep Learning
- 用LSTM、GRU、CNN-LSTM预测温湿度和二氧化碳
- GRU短期预测最准，CNN-LSTM适合长期预测
- 结果可指导智能空调系统，适合建筑节能研究者
保障室内环境质量（IEQ）对居住者健康与效率至关重要，但传统暖通空调（HVAC）系统往往能耗过高。本文提出一种基于深度学习的主动管理方法，针对二氧化碳浓度、温度和湿度等IEQ参数，在保证建筑能效的前提下实现优化控制。利用从零能耗学术建筑采集的ROBOD数据集，我们对比了三种模型架构：长短期记忆网络（LSTM）、门控循环单元（GRU）以及混合卷积神经网络-长短期记忆网络（CNN-LSTM），在不同时间尺度下预测IEQ变量。结果显示，GRU在短时预测中精度最高且计算开销低；CNN-LSTM在长周期预测中更擅长提取主导特征；而LSTM具备较强的长程时间建模能力。对比分析表明，预测可靠性受数据分辨率、传感器位置及动态人员流动影响。这些发现为智能建筑管理系统（BMS）实施预测性空调调控提供了可行方案，有助于降低能耗并提升实际运行中的居住舒适度。
原文摘要 · Abstract (English)
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.
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