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

Metamodel-based generative design of wind turbine foundations

Wind turbines play an integral role in energy transition agendas. The optimized design of wind turbine foundations is a complex and intricate task that requires iterative running of computationally-intensive and time-consuming finite element models. However, given the popularity of these structures over the past two decades, there is a wealth of data from the designs of the past projects that can be used for the data-driven modeling of these structures. Given the demonstrated accuracy and success of metamodels as an alternative approach for other computationally-intensive simulation-based problems, this study aims to develop a generative-design framework for the optimization of wind turbine foundations using a metamodel, as a complementary step to more accurate finite element modeling, to reduce the overall design time without compromising the accuracy. To this end, first, the random forest method is used to develop a multi-output metamodel for the wind turbine foundations based on a set of historical data. Then, a metaheuristic method, i.e., NSGA II, is adopted to optimize the design process based on the developed metamodel. In a case study, a wind turbine foundation was designed using the proposed framework and the accuracy of the output was assessed in terms of the ultimate bending moment. The results of the case study indicate that the proposed method provides a significant time gain (i.e., 99.93%) without compromising the accuracy (i.e., 1.75% for the percent error). Besides, the conducted study also offers designers a better understanding of the importance of each design variable and how certain design variables influence the moment-rotation behavior of the wind turbine foundatio