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.

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.

Amid China’s rapid urbanization, many historic districts face complex challenges, including fragmented traditional fabrics, disordered spatial morphology, and discontinuous street networks. To tackle these issues, this study proposes a multimodal deep learning framework that combines Generative Adversarial Networks (GANs) and Diffusion Models, establishing an integrated generation-optimization workflow for the renewal of historic districts. The methodology begins by using Pix2PixHD to generate high-precision fabric layouts, followed by fine-tuning a Diffusion Model through Low-Rank Adaptation (LoRA) to achieve diversified morphological expansion. The candidate proposals are quantitatively evaluated using a ten-indicator evaluation matrix that covers both architectural fabric and street network dimensions. Afterwards, these proposals undergo iterative optimization with a multi-objective framework to enhance both urban fabric morphology and network performance. The framework was validated through an empirical study of the Yuehe Historic District in Jiaxing. The results indicate that the generated schemes closely align with the original urban fabric. Compared with the existing expanded area (EA), the weighted comprehensive fitness score of the optimized scheme group improved from 0.66 to 0.89 ± 0.02 (a 34.8% increase), with the standard deviation decreasing from 0.07 to 0.02, indicating significantly enhanced stability. Deep learning balances morphological authenticity, generative diversity, and performance in historic district preservation and renewal.