With the rapid development of economy and urbanization in China, cities are expanding rapidly and more constructions are being built, then lead to the fast-growing part in urban residential energy consumption. In China’s hot summer and cold winter regions, many buildings are not centrally heated and commonly rely on electrical equipment such as air conditioners which are all energy-inefficient thermoregulation devices. In order to analyze the relationship between building energy consumption and the energy efficiency ratio (EER) of air conditioning and the area of photovoltaic (PV) on the roof, the influence of the building envelope on building energy consumption in hot summer and cold winter regions in China is clarified. This paper uses energy plus software to analyze the impact of different EER and PV area on building energy consumption using a typical case study of a public health upgrading project in the eastern part of the Wuxing district, Huzhou, Zhejiang province in China. The simulation results show that the factor that has a greater impact on the energy consumption of the health center in summer is the PV area parameter of the building compared to the performance parameters of the air conditioning equipment. The PV area parameter settings and air conditioning equipment performance adjustments are dependent on the actual situation and the comprehensive energy saving rate of the outpatient building for public health in the eastern part of Tai Wu Xing District can exceed 50%. Finally, an economic analysis of the carbon tax and input costs is carried out, and the best combination is of which 60% of the area covered by Longi Green PV panels and an air conditioning efficiency ratio of 4.87. The research result combines renewable energy and efficient equipment to achieve dual optimization of environmental and economic aspects of building energy consumption, while providing reference opinions on the comprehensive evaluation method of building energy consumption from the perspective of carbon tax.

Multi-objective optimisation is essential for balancing building energy efficiency and thermal comfort. Existing research primarily focuses on passive optimisation strategies that assume fixed behavioural patterns of a ‘rational occupant’. However, these studies often overlook the impact of stochastic occupant behaviour on building energy efficiency and thermal comfort. Furthermore, they fail to consider the inherent randomness, variability, dynamic nature, and feedback mechanisms of individual actions. As a result, this oversight can lead to suboptimal energy efficiency, insufficient thermal comfort, and a poor user experience. This study examines a naturally ventilated research building equipped with split-type air conditioning in China’s hot summer and cold winter climate zone. The research develops a rapid prediction model for air conditioning (AC) energy consumption and thermal comfort based on actual HVAC behaviours, incorporating the AC and natural ventilation (NV) operation schedules. The model utilises Artificial Neural Networks (ANNs), importance analysis, and batch simulation. Furthermore, a multi-objective optimisation decision-making model is developed to balance building AC energy consumption and indoor environmental thermal comfort, using the NSGA-II algorithm. The results indicate that when building design parameters comply with the current energy-saving design standards, behavioural optimisation can lead to a 31.4% reduction in energy use for building AC systems while enhancing thermal comfort by 37.5%. Furthermore, by implementing integrated optimisation strategies, comfort can be improved by as much as 92.6% without raising energy consumption.

Against the backdrop of global energy structure transformation, the intelligent scheduling of residential energy systems has emerged as a pivotal technology for enhancing energy utilization efficiency and reducing electricity costs. With the large-scale integration of renewable energy and the deepening reform of electricity marketization, RTP mechanisms are being progressively implemented in developed countries, creating new opportunities for demand-side response. Addressing the core contradiction of the current collaborative research on RTP and household energy scheduling, which faces a triple disconnection of “algorithm-mechanism-user”, this study considers the synergistic potential among electricity consumption behavior, supply and storage equipment, and electricity pricing mechanism in the household energy system. With the real-time electricity pricing mechanism as the core driver, intelligent scheduling models based on CPLEX, PSO, and DDPG are established respectively. Based on measured data, the scheduling strategies of different algorithms are simulated under seasonal typical day and typical month scenarios, and the electricity costs are compared. Furthermore, the performance of different algorithms in photovoltaic power generation, energy storage scheduling, and electricity trading is evaluated based on the simulation results, providing suggestions for matching algorithm selection strategies in different household energy consumption scenarios. The research results show that the CPLEX algorithm exhibits significant advantages in the long-term scheduling of the household energy system, reducing electricity costs by up to 54% compared to PSO and DDPG in the high-fluctuation market environment of winter. The findings of this study provide key technical references for constructing intelligent scheduling algorithms that couple dynamic electricity pricing mechanisms with complex energy consumption scenarios, and facilitate the transformation of household energy systems from “passive consumption” to “active profitability”.

Urban buildings stand as both a core source of carbon emissions and a key carrier of human settlements