Jiang Zixin, Deng Zhipeng, Wang Xuezheng, Dong Bing
Department of Mechanical & Aerospace Engineering, Syracuse University, Syracuse, NY 13244, United States.
Built Environment Science and Technology (BEST) Lab, Syracuse University, Syracuse, NY 13244, United States.
Appl Energy. 2023 Mar 15;334:120676. doi: 10.1016/j.apenergy.2023.120676. Epub 2023 Jan 22.
During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85 % accuracy using a one-person offset. The accuracy of 15 min of the ahead prediction could reach 95 % with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2 % for 93 % of the day. This OBMPC model could reduce the coil load by 52.44 % and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9 % to 26 % difference in demand ventilation, a 0.3 °C to 2.4 °C difference in zone temperature, a 28.5 % to 44.5 % difference in outdoor airflow rate, and a 10.7 % to 28.2 % difference in coil load.
在严重急性呼吸综合征冠状病毒2（SARS-CoV-2，即新冠病毒）大流行期间，世界各国政府纷纷制定政策，要求通风系统以更高的室外新鲜空气流速运行足够长的时间，这导致建筑能耗急剧增加。因此，有必要确定一种节能通风策略，以降低感染风险。在本研究中，我们为建筑通风系统开发了一种基于占用人数的模型预测控制（OBMPC）算法。首先，我们收集了2021年3月至7月的占用情况以及供暖、通风和空调系统（HVAC）数据。然后，使用四种不同的模型（基于自回归移动平均的多层感知器（ARMA_MLP）、递归神经网络（RNN）、长短期记忆网络（LSTM）以及带有变化点检测的非齐次马尔可夫模型（NH_Markov））提前15分钟至24小时预测房间内的占用人数，并进行间隔输出。我们发现，每个模型在采用一人偏移量时，都能以85%的准确率预测占用人数。提前15分钟预测的准确率在采用一人偏移量时可达到95%，但它们都无法跟踪突变情况。占用预测结果用于通过威尔斯-莱利方程计算通风需求，其上限可使93%的时间内感染风险保持在2%以下。与24×7小时全室外空气（OA）系统相比，当空间内人员佩戴口罩时，这种OBMPC模型可将盘管负荷降低52.44%，并将峰值负荷转移3小时，最高可达5千瓦。占用预测的不确定性可能导致需求通风量相差9%至26%，区域温度相差0.3°C至2.4°C，室外空气流速相差28.5%至44.5%，盘管负荷相差10.7%至28.2%。
