Zhi Zhuang

Urbanization in China and the proliferation of high-rise office buildings have led to increased demand for daylighting and thermal comfort. These requirements often result in reliance on active systems, including heating, cooling, and artificial lighting, which increase energy consumption. Existing studies have often focused on individual cases or room-scale models, which makes it difficult to generalize findings to the design of various high-rise office building types. Therefore, in this study, parametric prototype building models for high-rise office buildings were developed based on surveys of completed and under-construction projects. These surveys reflected actual design practices and were used to support systematic performance evaluation and typology-level optimization. Building performance was simulated using Grasshopper and Honeybee to generate large-scale datasets, and stacking ensemble learning models were used as surrogate predictors for energy use, daylighting, and thermal comfort. Multi-objective optimization was conducted using the non-dominated sorting genetic algorithm III (NSGA-III), followed by strategy formulation. The results revealed the following: (1) the proposed prototype model establishes clear parameter ranges for geometry, envelope design, and thermal performance, offering reusable models and data; (2) the stacking ensemble model outperforms individual models, improving the coefficient of determination (R²) by 0.5–16.1%, with mean squared error (MSE) reductions of 4.4–70.6%, and mean absolute error (MAE) reductions of 2.8–45.8%; (3) space length, aspect ratio, usable area ratio, window U-value, and solar heat gain coefficient (SHGC) were identified as primary performance drivers; and (4) optimized solutions reduced energy use by 3.79–11.81% and enhanced daylighting comfort by 40.16–50.32% while maintaining thermal comfort. The proposed framework provides localized, data-driven guidance for early-stage performance optimization in high-rise office building design.

Precision maintenance and operation of building systems are crucial for energy efficiency and carbon reduction in building sector. Fluctuations in people flow play a significant role in building cooling load. Traditional air handling unit (AHU) operating under constant air volume tends to waste energy, as it lacks the capability to dynamically adjust to varying demand. This study introduces an intelligent digital twin AHU system considering the people flow variation, and the energy-saving potential is evaluated by a commercial building in Shanghai. The IoT technology is used to monitor the parameters of the AHU, using MobileNetSSD and NMS algorithms to extract people flow information from a camera. Through sensitivity analysis of the collected data, the key parameters for load prediction are identified, forming the basis for establishing a dynamic load prediction model using ANN. A digital model of the AHU is set up by TRNSYS, and the optimal control strategy is determined to minimize fan and pump energy consumption under different cooling loads. The digital twin system has been successfully implemented in a shopping mall in Shanghai, and 29.5% reduction of fan energy consumption can be achieved by dynamically adjusting air supply rate in response to the changing cooling loads.