Surrogate modelling of solar radiation potential for the design of PV module layout on entire façade of tall buildings

This research investigated the performance of a surrogate modeling approach for the simulation of solar radiation potential on the vertical surfaces of tall buildings. Surrogate modeling is used to approximate the input–output behavior of the existing simulation model. The Random Forest (RF) machine learning approach was used to investigate three different scenarios, namely (1) Random variation, (2) Grid variation, and (3) Uniform variation, and the Genetic Algorithm is used to optimize the hyperparameters. A case study was performed to investigate the performance of surrogate models using a building in the Sir George William (SGW) campus of Concordia University in downtown Montreal Canada. The results suggest that even by only using a small sample size of the random solutions, surrogate modeling can achieve up to 94% accuracy in the prediction of solar radiation potentials. From the three scenarios, the best accuracy was obtained when using the Random variation method. In short, solar radiation simulation is very complex and too sensitive to the location and shadow effect. Therefore, simplification of those factors cannot be made to approximate the solar radiation potential. Also, using RF, the computational time improved by 16 times faster than when using the existing simulation model.