Na Yu, Fu Wenli, Guo Fei
During green building design, computer-aided energy assessment is widely used to improve efficiency and achieve overall optimization. This paper presents a platform that combines Building Information Modeling (BIM), sensor operational data, and advanced simulation workflows using robust algorithms. The platform uses a multi-layer service architecture with dynamic energy simulation and evolutionary multi-objective optimization, connected via a high-performance C++ core and adaptive agent models. A mid-rise office building was selected as the case study. Five representative areas were chosen to collect data on building envelope characteristics and occupancy patterns. After preprocessing, missing sensor data accounted for 3.2% of annual records, and all variables were standardized using 15-minute interpolation. After 40 optimization rounds, annual energy consumption per square meter dropped by 29.3% from 315 kWh/m2 to 223 kWh/m2. The lifecycle cost increase for occupants was limited to 3.7%, and discomfort hours were reduced to under 70 hours per year. Analysis of Pareto optimal solutions shows that the envelope U-value ranges from 1.05 to 1.57 W/m2K, and nighttime ventilation rate ranges from 2.1 to 3.6 h-1, both closely linked to energy performance. The results confirm that the integrated algorithm framework offers good scalability, strong performance, and technical feasibility for green building design. This platform provides a reliable decision-support tool for design engineers and sustainability practitioners, enabling accurate, data-driven delivery of energy-efficient buildings.