Ice-based thermal energy storage (TES) systems can shift peak cooling demand and reduce operational energy costs (with time-of-use rates) in commercial buildings. The accurate prediction of the cooling load, and the optimal control strategy for managing the charging and discharging of a TES system, are two critical elements to improving system performance and achieving energy cost savings. This study utilizes data-driven analytics and modeling to holistically understand the operation of an ice–based TES system in a shopping mall, calculating the system's performance using actual measured data from installed meters and sensors. Results show that there is significant savings potential when the current operating strategy is improved by appropriately scheduling the operation of each piece of equipment of the TES system, as well as by determining the amount of charging and discharging for each day. A novel optimal control strategy, determined by an optimization algorithm of Sequential Quadratic Programming, was developed to minimize the TES system's operating costs. Three heuristic strategies were also investigated for comparison with our proposed strategy, and the results demonstrate the superiority of our method to the heuristic strategies in terms of total energy cost savings. Specifically, the optimal strategy yields energy costs of up to 11.3% per day and 9.3% per month compared with current operational strategies. A one-day-ahead hourly load prediction was also developed using machine learning algorithms, which facilitates the adoption of the developed data analytics and optimization of the control strategy in a real TES system operation.

A detailed model of heat and moisture transfer through clothing is developed and has been implemented in the UC Berkeley Comfort Model (UCB Model), to simulate dry heat loss and moisture absorption/evaporation through clothing. Equations are presented to describe four paths of heat and moisture transfer between naked skin and environment, clothed skin and environment, and naked and clothed skin in direct contact with a solid surface. Case studies are presented for different typical clothing ensembles and different contact fractions of the contact area with a solid surface, a chair. The results show that the developed clothing model is a useful tool for the study of heat and moisture transfer through clothing and contact surfaces, and the assessment of thermal comfort in transient, non-uniform environments.

A detailed model of heat and moisture transfer through clothing has been developed and implemented in the multi-segment UC Berkeley Thermophysiological Comfort model (BTCM). Equations are presented for two paths of heat and moisture transfer, between naked skin and environment, and clothed skin and environment. Transient behavior due to absorption and desorption by clothing is included. Segment-specific values for clothing insulation, vapor resistance, and the effects of air movement and walking are estimated from various sources. The new model is shown to simulate results from empirical studies with good accuracy. Parametric simulations are done to evaluate the physiological and comfort influences of the airspeed correction equations, and the heat transfer effects of different clothing levels at different temperatures. The results quantify the substantial air velocity and air temperature impacts on thermal physiology and thermal comfort. It can be seen that the new model is useful for studying heat and moisture transfer through clothing, and evaluating thermal comfort in transient environments.

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.