Near-wall microenvironment of a building refers to parameters such as wind speed, temperature, relative humidity, solar radiation near the building’s façade, etc. The distribution of these parameters on the building façade shows a certain variation based on changes in height. As a technology of passive heating and ventilation, the effectiveness of this application on heat collection wall is significantly affected by the near-wall microclimate, which is manifested by the differences, and rules of the thermal process of the components present at different elevations. To explore the feasibility and specificity of this application of heat collection wall in high-rise buildings, this study uses three typical high-rise buildings from Zhengzhou, China, as research buildings. Periodic measurements of the near-wall microclimate during winter and summer were carried out, and the changing rules of vertical and horizontal microclimate were discussed in detail. Later, by combining these measured data with numerical method, thermal process and performance of heat collection wall based on increasing altitude were quantitatively analyzed through numerical calculations, and the optimum scheme for heat collection wall components was summarized to provide a theoretical basis for the structural design of heat-collecting wall in high-rise buildings.

Since the 21st century, in several public health emergencies that have occurred across the world, the humid enclosed environment of the toilet has become one of the places where bacteria, viruses, and microorganisms breed and spread. Mobile public toilets, as a supplement of urban fixed public toilets, are also widely used in densely populated areas. According to statistics, since the outbreak of COVID-19 in 2019, multiple incidents of people being infected by the COVID-19 virus due to aerosol proliferation in public toilets have been confirmed. It is an urgent issue to resolve the internal environmental pollution of mobile public health and reduce the risk of virus transmission in public spaces under the global epidemic prevention. This paper utilized a typical combined mobile public toilet as the research object and measured and evaluated the indoor thermal environment in real time over a short period of time. The diffusion mode and concentration change law of pollutants in mobile public toilets were predicted and analyzed based on CFD. Regression analysis was also used to clarify the relationship between indoor thermal environment variables and aerosol diffusion paths, and a ventilation optimization scheme was proposed to reduce the risk of virus transmission.

Commercial building demand response control is an effective way to reduce summer electrical peak demand in cities. However, a portfolio of a large number of buildings has different demand response control strategies than individual buildings. In this article, an optimization method is proposed for scheduling demand response strategies for multiple commercial buildings that can be recognized as a multi-building portfolio during demand response. Through this optimal scheduling, decision makers can determine which buildings should participate in the demand response program that controls the strategies that should be used as well as the corresponding optimum starting/ending time. In this article, the optimal scheduling problem is converted into a time series 0-1 programming problem and is solved by the Branch and Bound method, with three optimization objectives of smooth reduction, maximum reduction, and maximum economic benefits. As a pilot demand response case test, the measured data for 18 large commercial buildings (as a multi-building portfolio) in a recent large-scale demand response experiment were used to evaluate the effectiveness of the optimal scheduling method. The results demonstrated that the optimal scheduling scheme can significantly improve the demand response effectiveness of a large number of buildings together.

The large-scale rural home hotel clusters have brought huge pressure to the rural power grid. However, the load of rural home hotels not only has the inherent characteristics of rural residential buildings but is also greatly impacted by the occupancy rate, which is very different from conventional buildings. Therefore, the existing peak shifting strategies are difficult to apply to rural home hotels. In view of the above problems, this study took a typical visitor village in Zhejiang Province as the research object, which had more than 470 rural home hotels. First, through a basic information survey and power load data collection, the characteristics of its power load for heating, cooling and transition months were studied, and a “No Visitors Day” model was proposed, which was split to obtain the seasonal load curve for air conditioning. Then, combined with the characteristics of the air conditioning power load and the natural conditions of the rural house, a cluster control peak-load-shifting system using phase change energy storage was proposed, and the system control logic was determined and established. Finally, the collected power load data was brought into the model for actual case analysis to verify its feasibility and the effect of peak-load shifting. The results showed that due to the influence of the number of tourists, the electricity loads on weekends and holidays were higher, especially the electricity load of air conditioning equipment in the heating and cooling seasons. An actual case was simulated to verify the peak-shifting effect of the proposed regulation strategy; it was found that the maximum peak load of the cluster was reduced by 61.6%, and the peak–valley difference was 28.6% of that before peak shifting.

Forecasting and intelligent operation of HVAC systems in public buildings are crucial for energy conservation, carbon reduction, and enhancing occupant comfort. This area has seen significant progress. However, for intermittent HVAC systems, noise reduction techniques applied to periodic load sequences may distort the load during start–stop transitions. To tackle this challenge, an innovative hybrid prediction method, GCMWSG-HOA-LightGBM, is proposed in this study. The GCMWSG filter utilizes period division and boundary protection mechanisms to avoid cross-period coupling errors in traditional filters, preserving the authenticity of cooling load start–stop transitions. Additionally, the HOA is employed to automatically optimize the hyperparameters of the LightGBM model, enhancing both parameter tuning efficiency and prediction accuracy. The proposed model is validated using operational data from an office building in Foshan, Guangdong Province, and its performance is compared with several hybrid models: HOA-LightGBM, BWO-LightGBM, AO-LightGBM, TPE-LightGBM, HOA-RF, BWO-RF, AO-RF and TPE-RF. The results show that the GCMWSG-HOA-LightGBM model outperforms all comparison models across all metrics, achieving R², MAPE, and CVRMSE values of 92.22%, 7.05%, and 9.46%, respectively, highlighting its accuracy and stability. The findings provide an efficient solution for intelligent load prediction of HVAC systems with intermittent and periodic characteristics, offering valuable engineering insights and theoretical guidance.