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

Vahdatikhaki, Faridaddin; Barus, Meggie Vincentia; Shen, Qinshuo; Voordijk, Hans; Hammad, Amin

doi:10.1016/j.enbuild.2023.112958

Cited by 9 publications

(1 citation statement)

References 45 publications

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“…The problem regarding how power effective the PV panels placed on the building's façade are examined by Vahdatikhaki et el. [34]. Surrogate modeling is applied to simulate solar radiation as the last one is predicted via the RF algorithm in three scenarios.…”

Section: State-of-the-artmentioning

confidence: 99%

Development of AI-Based Tools for Power Generation Prediction

Aravena-Cifuentes,

Nuñez-Gonzalez,

Elola

et al. 2023

Computation

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This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination, R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.

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Section: State-of-the-artmentioning

confidence: 99%