论文标题
基于LSTM的空间占用预测对有效的建筑能源管理
LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management
论文作者
论文摘要
建筑物中消耗的能源占全球总能源使用情况的很大一部分。大量建筑能源用于加热，冷却，通风和空调（HVAC）。但是，与其重要性相比，如今的建筑能源管理系统受到基于简单规则控制（RBC）技术的HVAC的限制。设计可以有效管理HVAC的系统的能力可以减少能源使用和温室气体排放，总而言之，它可以帮助我们缓解气候变化。本文提出了使用LSTM的占用模式的预测时间序列模型。下一个时间跨度（例如，接下来30分钟）在未来房间占用状态的预测信号可直接用于操作HVAC。例如，根据预测并考虑冷却或加热的时间，可以在使用房间之前打开HVAC（例如，提前10分钟打开）。另外，根据隔壁房间空的预测时间，可以更早地关闭HVAC，它可以帮助我们提高HVAC的效率，同时又不降低舒适性。我们使用从大学建筑的多个房间收集的现实世界能量数据来证明我们的方法的功能。我们表明，与基于常规的RBC的控制相比，LSTM的基于HVAC控制的基于HVAC的HVAC控制可能会节省50％。
Energy consumed in buildings takes significant portions of the total global energy usage. A large amount of building energy is used for heating, cooling, ventilation, and air-conditioning (HVAC). However, compared to its importance, building energy management systems nowadays are limited in controlling HVAC based on simple rule-based control (RBC) technologies. The ability to design systems that can efficiently manage HVAC can reduce energy usage and greenhouse gas emissions, and, all in all, it can help us to mitigate climate change. This paper proposes predictive time-series models of occupancy patterns using LSTM. Prediction signal for future room occupancy status on the next time span (e.g., next 30 minutes) can be directly used to operate HVAC. For example, based on the prediction and considering the time for cooling or heating, HVAC can be turned on before the room is being used (e.g., turn on 10 minutes earlier). Also, based on the next room empty prediction timing, HVAC can be turned off earlier, and it can help us increase the efficiency of HVAC while not decreasing comfort. We demonstrate our approach's capabilities using real-world energy data collected from multiple rooms of a university building. We show that LSTM's room occupancy prediction based HVAC control could save energy usage by 50% compared to conventional RBC based control.
