Predicting Vertical Daylight Illuminance Data From Measured Solar Irradiance: A Machine Learning-Based Luminous Efficacy Approach

Daylight data are required for energy-efficient building designs. However, daylight is scarcely measured, making the luminous efficacy model an alternative. This paper presents a method for modeling vertical luminous efficacy (K_vg) using measured data from measuring stations in Hong Kong. The artificial neural network (ANN), support vector machines (SVM), and empirical correlations were proposed for modeling K_vg. Machine learning (ML) models like ANN and SVM were used because they offer more accurate daylight predictions and ease in explaining complex relationships between atmospheric variables. Also, ML was explored since it has not been used in earlier vertical luminous efficacy studies. Sensitivity analysis was also carried out to determine the relative importance of input variables used for developing the proposed models. Findings show that scattering angle and diffuse fraction are crucial variables in vertical luminous efficacy modeling. Furthermore, when all proposed models were used to predict vertical daylight, it was observed that the peak relative root mean square error (%RMSE) was less than 18.6%. The obtained %RMSE showed that all models provided acceptable performance when evaluated against the measured daylight data. Finally, the findings also showed that the ANN models outperformed the SVM and empirical models.