Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO₂ meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO₂ generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO₂ readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO₂ generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO₂ levels. The occupancy schedule becomes critical when the CO₂ data are limited, whereas a 15-min measurement interval could capture dynamic CO₂ profiles well even without the occupancy information. Hourly CO₂ recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m³ and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m³ and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m³ and 19 occupancies), 0.40±0.32, 0.48±0.37, 0.72±0.39 ACH for Room #4 (231 m³ and 8–9 occupancies) for three consecutive days.

Road tunnels consume a large amount of energy, especially in the Canadian cold climate, where the roads are heated electrically or deicing during the winter. For a more sustainable and resilient road tunnel energy system, we conducted an exploratory study on installing a semi-transparent photovoltaic (STPV) canopy at the entrances and exits of a tunnel under a river. The proposed system generates solar-powered electricity, improves thermal and visual conditions, and reduces energy loads. In this study, field measurements of road surface temperature and air temperature were conducted, and numerical simulations with and without STPV were performed to study air and road surface temperatures under different traffic speeds. The field measurements show the road surface temperatures are higher than the air temperature on average. The interior air and road surface temperature were measured to be above 0 ℃, even though the outdoor temperature is far below 0 ℃, thus significantly reducing the need for deicing in winter using salts. The simulations show that the air and surface temperatures elevate due to the solar transmission heat through the STPV canopy, thus reducing deicing energy consumption significantly. The fire safety analysis also showed that the proposed system's top opening should be located near the tunnel entrance instead of the canopy entrance for better smoke exhaust during a fire.

Combining natural and mechanical ventilation, hybrid ventilation is an effective approach to reduce cooling energy consumption. Although most existing control strategies for HVAC systems with hybrid ventilation provide acceptable operation results, there still often exists a mismatch of demand and response from sensing, decision making, and operating. Especially when using renewable energy sources, such as solar and thermal storage, many energy-saving decisions need to be made before the actual events may happen. As a result, predictive-based controls are preferred, and the future energy loads and saving potentials from renewable measures should be evaluated in a forecasted manner. Typical prediction simulation methods are developed for designs and analysis, which may not ensure the required accuracy for modeling future events. In this study, a novel data assimilation method originating from numerical weather prediction, Ensemble Kalman Filter (EnKF), was proposed and applied for the forecasting simulations of high-rise building cooling load and energy-saving potential from its hybrid ventilation system. Similar to an accurate short-term weather prediction process, the proposed EnKF method can ensure the simulation accuracy by combining numerical simulations and measured data for short-term forecasting of future events. In the EnKF algorithm, a simulation model is adjusted according to the measuring data to output more accurate predictive results of the cooling load reduction from a hybrid ventilation system. Based on these predictions, the supply air temperature can be adjusted, and the duration of applying natural ventilation in real-time to maintain the desired comfort of building occupants with less energy consumption than existing strategies. The proposed forecasting model can be used in real life when combined with smart building controls. The results show that the proposed EnKF method improves the accuracy of the predicted velocities. The key EnKF parameters, Kalman filter gain, and the number of ensemble members are discussed as well. With the localized Kalman filter, the average RMSE and CVRMSE decrease by 46.4% and 53.5%, respectively.

The efficiency of air curtain in reducing infiltration and associated energy usage is currently evaluated statically by using an efficiency factor, η_air, based on single steady/static condition, which is often not the case for actual buildings under variable weather conditions and door usages. Based on a new method to consider these dynamic effects on air curtains, this study uses a dynamic efficiency factor η_B in terms of whole building site end-use energy to assess the efficiency of air curtains when compared to single doors (i.e. without air curtains) and vestibule doors. Annual energy simulations were conducted for two reference building models considering their specific door usage schedules in 16 climate zone locations in the North America. The variations of the proposed efficiency factor for different climate zones illustrated the dynamic impacts of weather, building, unit fan energy and door usage frequency on air curtain efficiency. A sensitivity study was also conducted for the operation temperature conditions of air curtain and showed that η_B also considers these operational conditions. It was thus concluded that using whole building site end-use energy to calculate the efficiency factor, η_B, can provide more realistic estimates of the performance of air curtains operations in buildings than the existing static efficiency factor.