Problem
Research questions and friction points this paper is trying to address.
green building
energy-efficient design
Building Information Modeling
energy optimization
simulation workflow
Application of Algorithms in Energy-Efficient Design Platforms for Green Building
🤖 AI Summary
This study addresses the lack of efficient co-optimization tools in green building design that simultaneously balance energy efficiency, cost, and thermal comfort. The authors propose a multilayer service platform integrating Building Information Modeling (BIM), high-fidelity sensor data, and dynamic energy simulation. By innovatively combining an adaptive agent-based model with a high-performance C++ core algorithm and employing evolutionary multi-objective optimization, the platform enables effective multi-criteria协同 design. Validated over 40 optimization iterations, the approach reduces annual energy consumption per unit area by 29.3% (from 315 to 223 kWh/m²), increases user life-cycle cost by only 3.7%, and maintains annual discomfort hours below 70. These results demonstrate the platform’s significant advantages in performance, scalability, and practical engineering applicability.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-objective optimization
dynamic energy simulation
adaptive agent models
Building Information Modeling (BIM)
energy-efficient design
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