Characterizing Architectural Glazing Performance for Circadian Light.

Growing research on the non-visual impacts of light underscores the importance of architectural glazing systems in managing transmitted shortwave solar light and shaping indoor circadian light, vital for enhancing well-being. This study, conducted in two phases, evaluates the effectiveness of existing window properties in predicting their contribution to circadian lighting. Initially, a decision tree analysis assessed these properties and revealed that although traditional glazing metrics are not entirely accurate for circadian performance estimations, they can still be effective when supplemented with specific thresholds as rapid tools for selecting windows optimized for circadian health. The second phase introduced 'circadian transmittance' (Tc), a new metric measuring window transmittance tailored to the human circadian action spectra. Various machine-learning models were applied to assess the efficacy of Tc and other glazing properties in predicting these systems' circadian lighting potential. The analysis demonstrated that Tc-based methods yield more accurate predictions under conditions of high solar angles and clear skies, but their accuracy decreases in cloudy conditions and at low solar angles. In conclusion, this research significantly advances the field by proposing an analytical framework that empowers architects and engineers to make informed decisions to enhance indoor environmental health. The development of circadian transmittance-based machine learning models not only provides crucial insights into the impact of glazing properties on window systems' circadian performance but also sets the stage for future standards in fenestration's circadian metrics. These contributions are poised to influence building design and occupant health, marking a substantial step forward in environmental and architectural science.