A radiant floor cooling system (RFCS) is a high-comfort and low energy consumption system suitable for residential buildings. Radiant floor systems usually work with fresh air, and their operating performance is affected by climatic conditions. Indoor and outdoor environmental disturbances and the system’s control strategy affect the indoor thermal comfort and energy efficiency of the system. Firstly, a multi-story residential building model was established in this study. Transient system simulation program was used to study the operation dynamics of three control strategies of the RFCS based on the calibrated model. Then, the performance of the control strategies in five climate zones in China were compared using multi-criteria decision-making in combination. The results show that control strategy has a negligible effect on condensation risk, but the thermal comfort and economic performance differ for different control strategies. The adaptability of different control strategies varies in different climate zones based on the consideration of multiple factors. The performance of the direct-ground cooling source system is better in Hot summer and warm winter zone. The variable air volume control strategy scores higher in Serve cold and Temperate zones, and the hours exceeding thermal comfort account for less than 3% of the total simulation period. Therefore, it is suggested to choose the RFCS control strategy for residential buildings according to the climate zone characteristics, to increase the energy savings. Our results provide a reliable reference for implementing RFCSs in residential buildings.

Energy simulation results for buildings have significantly deviated from actual consumption because of the uncertainty and randomness of occupant behavior. Such differences are mainly caused by the inaccurate estimation of occupancy in buildings. Therefore, the error between reality and prediction could be largely reduced by improving the accuracy level of occupancy prediction. Although various studies on occupancy have been conducted, there are still many differences in the approaches to detection, prediction, and validation. Reports published within this domain are reviewed in this article to discover the advantages and limitations of previous studies, and gaps in the research are identified for future investigation. Six methods of monitoring and their combinations are analyzed to provide effective guidance in choosing and applying a method. The advantages of deterministic schedules, stochastic schedules, and machine-learning methods for occupancy prediction are summarized and discussed to improve prediction accuracy in future work. Moreover, three applications of occupancy models—improving building simulation software, facilitating building operation control, and managing building energy use—are examined. This review provides theoretical guidance for building design and makes contributions to building energy conservation and thermal comfort through the implementation of intelligent control strategies based on occupancy monitoring and prediction.