Solar energy forecasting models using Python
DOI:
https://doi.org/10.33448/rsd-v13i8.46500Keywords:
Modeling; NASA; Artificial intelligence; Support vector machine; Artificial neural networks.Abstract
The main objective of this study is to provide a clear and systematic framework for data collection, preparation, modeling, evaluation and analysis of the results obtained. This study explores the potential of artificial intelligence (AI) models to predict solar radiation in Belém-PA, with a view to optimizing solar energy generation in the region. By analyzing data from the NASA POWER satellite (2024), several regression models were implemented and evaluated, including Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Gradient Boosting Tree (GBT), Multivariate Adaptive Regression Spline (MARS) and Classification and Regression Tree (CART). The results show that Random Forest stands out in terms of average accuracy, while MARS and GBT are more robust in generalizing the data. Cross-validation and the analysis of metrics such as RMSE and MBE prove the importance of assessing the reliability of the models. However, the anomalous performance of CART, with an RMSE of 0.0 in both evaluations, requires investigation to verify the existence of overfitting. In summary, this study highlights the potential of AI models for predicting solar radiation in Belém-PA, with Random Forest, MARS and GBT presenting themselves as promising models for solar energy forecasting applications. There is a need for more comprehensive cross-validation and investigation of CART's performance to ensure the robustness and reliability of the results, driving the optimization of solar energy generation in the region.
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Copyright (c) 2024 Camila Piacitelli Tieghi; Carlos Educardo Camargo Nogueira; Jair Antonio Cruz Siqueira; Carlos Roberto Souza Carmo; Luís Fernando Soares Zuin; Jorge Alvarez; Fernando de Lima Caneppele
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