Contrasting accuracies of single and ensemble models for predicting solar and thermal performances of traditional vaulted roofs

Traditional curved-roof forms have significant potentials in mitigating undesirable environmental impacts. Their performance predictions can be grouped into 4 trendlines of varying degrees of sophistication: theoretical abstracts, numerical methods, white-box simulations and black-box machine learning algorithms. Unprecedently, this research investigates the potential contribution of single- and ensemble-models to approximate the average hourly direct normal and diffuse horizontal irradiances (AHIRDirect, AHIRDiffuse) and cooling energy consumption (AHECCooling) of buildings topped with vaulted-roof forms of various configurations in Aswan, Egypt. Solar and energy simulations are first conducted to build essential datasets, which get pre-processed, before developing 8 single-models, representing 4 families of supervised single-algorithms: artificial neural networks, random forests, k-nearest neighbors and support vector regression. Voting ensemble-model is then created by combining the best-performing single-models. Lastly, the accuracies of all models are compared against simulation outputs. The results showed that no single-model could dominantly predict AHIRDirect, AHIRDiffuse and AHECCooling, obtaining tolerable R2 values, ranging from 97.017 to 61.913%, 92.782 to 43.986% and 99.341 to −9.219%, corresponding to RMSE values of 47.321 to 195.208, 17.457 to 53.617 and 0.002 to 0.032, respectively. Alternatively, voting ensemble-model acquired even better R2 values of 93.971, 93.047 and 97.276%, with RMSE values of 69.000, 17.249 and 0.004, respectively.