| id |
ijac202321413 |
| authors |
Ayoub, Mohammed |
| year |
2023 |
| title |
Estimating the received solar irradiances by traditional vaulted roofs using optimized neural networks and transfer learning |
| source |
International Journal of Architectural Computing 2023, Vol. 21 - no. 4, 795-820 |
| summary |
Traditional vaulted roof-forms have long been utilized in hot-desert climate for better indoor environmental quality. Unprecedently, this research investigates the possible contribution of machine learning to estimate the received solar irradiances by those roofs, based on simulation-derived training and testing datasets, where two algorithms were used to reduce their higher-dimensionality. Then, four models of ordinary least-squares and artificial neural networks were developed. Their ability to accurately estimate solar irradiances was confirmed, with R2 of 95.599–98.794% and RMSE of 12.437–23.909 Wh/m2. Transfer Learning was also applied to pass the stored knowledge of the best-performing model into another one for estimating the performance of new roof-forms. The results demonstrated that transferred models could provide better estimations with R2 of 87.416–97.889% and RMSE of 79.300–13.971 Wh/m2, compared to un-transferred models. Machine learning shall redefine the practice of building performance, providing architects with flexibility to rapidly make informed decisions during the early design stages. |
| keywords |
Solar irradiance, prediction, simulation, machine learning, transfer learning |
| series |
journal |
| references |
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