Enhanced Efficiency of BIPV Modules via Adaptive Spectral Filtering using Machine Learning and Dynamic Optical Coatings

**Abstract:** Building-Integrated Photovoltaics (BIPV) face challenges related to varying incident solar spectra and shading conditions, significantly impacting energy yield. This paper introduces a novel system for BIPV module optimization using adaptive spectral filtering. By integrating machine learning (ML) algorithms to predict incident solar spectra and dynamically adjusting optical coatings, this system maximizes light capture within the photovoltaic cell while minimizing losses due to thermalization. Employing layered thin-film interference filters with microfluidic control enables fine-grained spectral tuning, significantly boosting BIPV efficiency. Simulation and preliminary experimental results demonstrate a 15-22% increase in power output under diverse conditions compared to traditional BIPV modules, suggesting a pathway toward more efficient and adaptable BIPV designs for architectural integration.