The fixed description of HVAC behaviours leads to inaccurate prediction of air conditioning energy consumption, which in turn affects the appropriateness and effectiveness of energy conservation strategies. Based on a naturally ventilated research building located in Hangzhou, China, a stochastic prediction model reflecting actual HVAC behaviours is established based on clustering analysis and the Monte Carlo method, and it is integrated into the AC energy consumption simulation through Python programming. Then, important factors influencing AC energy consumption are clarified by importance analysis based on random forest regression, and the integrated strategies based on them are studied based on the simulation and control variable approach. As a result, the error rate between the measured and simulated AC power consumption is −5.24% and 2.56% in the heating and cooling conditions, respectively. And the relative importance and the number of important factors following the actual HVAC behaviours are remarkably different from those based on the fixed behavioural pattern. The implementation of integrated AC energy conservation strategies based on important influencing factors achieves 35.02% energy savings. Consequently, a theoretical basis for the accurate prediction of AC energy consumption and efficient implementation of energy conservation strategies is established.

Due to the influence of surrounding buildings on the radiation transfer process, the irradiance of individual buildings in building stocks is more uneven and different than that of individual buildings in open spaces. In view of the defect of the existing building surface irradiance calculation model in the sky radiation energy balance calculation, the complex surface reflection radiative transfer in diffuse irradiance, and complex processes, this paper combined the calculation of the complex surface narrow sky view, multiple reflections, and radiation characteristics of nonuniformity, and finally established the model for irradiance on the facade of a building stock (IFBS model) in a sheltered environment. The simulation results show that the IFBS model is superior to the traditional model in the calculation of sky diffuse irradiance and reflection irradiance of building stocks and is more suitable for the numerical calculation of the radiation transfer process of complex buildings.

Against the background of China’s dual carbon goals and the large-scale integration of renewable energy, building-side energy flexibility is of great significance for alleviating peak load pressure on the power grid, improving renewable energy accommodation, and reducing operational carbon emissions. This study investigates a nearly zero-energy residential building in the hot-summer and cold-winter climate zone of China. A TRNSYS-based dynamic model of a solar–air-source heat pump radiant floor heating system was developed to evaluate the effects of solar collector area, rated heat pump heating capacity, thermal storage tank volume, and heat pump start–stop center temperature on energy flexibility potential under the H1 strategy. Identified in the preceding study as having the greatest flexibility potential among the strategies evaluated, H1 switches off the heat pump throughout the peak period from 08:00 to 22:00, while solar heat and thermal energy stored in the tank provide space heating. Acceptable thermal comfort in all heated rooms was demonstrated under the reference configuration. A one-factor-at-a-time analysis and Spearman rank correlation analysis were conducted using two flexibility indicators: reduction in peak-period operating energy consumption and increase in solar heat collection relative to the corresponding baseline scenario. Peak-period carbon emissions were also evaluated. The collector area showed a strong negative association with peak-period electricity reduction (ρ = −0.97) and a strong positive association with increased solar heat collection (ρ = 0.82). The start–stop center temperature showed positive associations with both indicators, whereas the rated heating capacity and tank volume showed weaker associations. This study provides useful guidance for optimizing system configuration, reducing peak loads on the power grid, promoting renewable energy accommodation, and supporting the low-carbon operation of buildings.