The cold accumulation problem can lead to performance degradation of heat pumps. This paper presents the design and optimization of a solar-assisted storage system to solve this issue. A ground source heat pump (GSHP) project was established using the transient system simulation program (TRNSYS) based on a ground heat exchange theoretical model, which was validated by a previously established experiment in Beijing. The Beijing, Harbin, and Zhengzhou regions were used in numerical simulations to represent three typical cities where buildings require space heating (a cold region, a severe cold region, and a hot summer and cold winter region, respectively). System performance was simulated over periods of ten years. The simulation results showed that the imbalance efficiencies in the Beijing, Harbin, and Zhengzhou regions are 55%, 79%, and 38%, respectively. The annual average soil temperature decreases 7.3°C, 11.0°C, and 5.3°C during ten years of conventional GSHP operation in the Beijing, Harbin, and Zhengzhou regions, respectively. Because of the soil temperature decrease, the minimum heating coefficient of performance (COP) values decrease by 23%, 46%, and 11% over the ten years for GSHP operation in these three regions, respectively. Moreover, the simulation data show that the soil temperature would still be decreasing if based on the previous solar energy area calculation method. Design parameters such as the solar collector size are optimized for the building load and average soil temperature in various cold regions. Long-term operation will test the matching rate of the compensation system with the conventional GSHP system. After the system is optimized, the solar collector area increases of 20% in the Beijing region, 25% in the Harbin region, and 15% in the Zhengzhou region could help to maintain the annual average soil temperature balance. The optimized system could maintain a higher annual average COP because of the steady soil temperature. It provides a method for the design of a solar collector area which needs to be determined in the seasonal heat storage solar ground source heat pump system.

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs).

Indoor temperature and relative humidity control in office buildings is crucial, which can affect thermal comfort, work efficiency, and even health of the occupants. In China, fan coil units (FCUs) are widely used as air-conditioning equipment in office buildings. Currently, conventional FCU control methods often ignore the impact of indoor relative humidity on building occupants by focusing only on indoor temperature as a single control object. This study used FCUs with a fresh-air system in an office building in Beijing as the research object and proposed a deep reinforcement learning (RL) control algorithm to adjust the air supply volume for the FCUs. To improve the joint control satisfaction rate of indoor temperature and relative humidity, the proposed RL algorithm adopted the deep Q-network algorithm. To train the RL algorithm, a detailed simulation environment model was established in the Transient System Simulation Tool (TRNSYS), including a building model and FCUs with a fresh-air system model. The simulation environment model can interact with the RL agent in real time through a self-developed TRNSYS–Python co-simulation platform. The RL algorithm was trained, tested, and evaluated based on the simulation environment model. The results indicate that compared with the traditional on/off and rule-based controllers, the RL algorithm proposed in this study can increase the joint control satisfaction rate of indoor temperature and relative humidity by 12.66% and 9.5%, respectively. This study provides preliminary direction for a deep reinforcement learning control strategy for indoor temperature and relative humidity in office building heating, ventilation, and air-conditioning (HVAC) systems.

With increasing global climate change, extreme weather threats to indoor environments are growing. Heatwave events provide essential data for building thermal resilience analysis. However, existing heatwave definition indicators vary widely and lack standardized criteria. To more accurately evaluate indoor overheating risks, this study compared indoor overheating responses under different heatwave definition indicators, considering the temporal disconnect between indoor and outdoor heat conditions. Focusing on Beijing, this study established an indoor–outdoor coupled heatwave evaluation framework using 1951–2021 meteorological data and the heat index as an overheating metric. By analyzing indoor overheating degree and overlap degree to characterize indoor–outdoor correlations, we concluded that different definitions of heatwaves lead to variations in identifications, while multidimensional indicators better capture extreme events. Heatwaves with prolonged duration and high intensity pose greater health risks. Although Beijing’s indoor thermal conditions are generally safe, peak heat indices during summer heatwaves exceed danger thresholds in some buildings, highlighting thermal safety concerns. The metrics for heatwave 6 and heatwave 7 optimally integrate indoor–outdoor characteristics with higher thresholds identifying more extreme events. These findings support the design of building thermal resilience, overheating early warnings, and climate-adaptive electrification strategies.