An Intelligent Eco-Visual Identity Framework for Adaptive Green Buildings

This study tackles the challenge of optimizing aesthetics, energy use, and material sustainability for eco-friendly visual identity systems in dynamically operated green buildings. A “Metric-Simulation-Control-Evaluation” closed-loop framework was developed. It integrates multi-objective entropy weight assessment, light-thermal simulation, and a vector quantized-variational autoencoder-based color-footprint material library, supporting the generation and selection of sustainable visual solutions. In operation, an adaptive dimming system using the twin delayed deep deterministic policy gradient reinforcement learning algorithm enabled real-time coordinated control of lighting and shading. Experiments demonstrated annual electricity savings of 12,412 kWh and increased user satisfaction to 4.5 (on a five-point scale), confirming synergistic gains in energy conservation, environmental impact, and experience. This work provides a practical pathway for dynamic, intelligent ecological visual interaction in green buildings.