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

•Solar and thermal performances of traditional vaulted roofs were investigated.•Machine learning presents rapid predictions with equivalent accuracy to simulations.•4 families of machine learning algorithms representing 8 single-models were created.•A voting ensemble-model was developed based on the best-performing single-models.•The results showed that no single-model could solely dominate the predictions.•The ensemble-model could perform better than any of its constituent single-models. 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.