Intelligent design of multispectral regulation transparent passive cooling energy-saving window based on genetic algorithm

Multispectral selective windows represent a significant opportunity for enhancing building energy efficiency, but their widespread adoption has been constrained by the complexity and cost of the micro- and nano-photonic structures they typically require. This study directly confronts this limitation by presenting a genetically algorithm (GA)-optimized, highly transparent passive cooling (TPC) window. The design achieves a remarkable balance between architectural simplicity and high-performance multispectral management. Utilizing a computationally driven co-optimization process, the GA tailors the best six-layer TiO2/SiO2 thin-film stack based on a coupled reflective-absorptive synergistic mechanism. This designed and fabricated TPC window demonstrates outstanding spectral selectivity: high visible transparency (Tlum = 83.1%) alongside strong UV (TUV = 12.0%) and NIR (TNIR = 17.8%) shielding capabilities. Furthermore, the window exhibits high mid-infrared thermal emissivity (exceeding 94%) within the atmospheric transparency window (8–13 μm), facilitating efficient radiative heat dissipation. Indoor/outdoor tests indicate significant temperature reductions: 8.3 °C (indoor)/6.1 °C (outdoor) versus ordinary glass, and 10.5 °C (indoor)/6.2 °C (outdoor) versus ITO glass. EnergyPlus simulations demonstrate that the TPC window enables over 20% annual energy savings in tropical and subtropical regions. The GA-optimized window attains high performance via a facile six-layer structure and fabrication process, offering a commercially viable pathway for large-scale deployment of energy-efficient glazing and contributing directly to sustainable energy development.