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Your search keyword '"Ramos, Diogo Nunes da Silva"' showing total 15 results
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15 results on '"Ramos, Diogo Nunes da Silva"'

3. Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Author

Araujo, Mirella Lima Saraiva, primary, Kitagawa, Yasmin Kaore Lago, additional, Weyll, Arthur Lúcide Cotta, additional, Lima, Francisco José Lopes de, additional, Santos, Thalyta Soares dos, additional, Jacondino, William Duarte, additional, Silva, Allan Rodrigues, additional, Filho, Márcio de Carvalho, additional, Bezerra, Willian Ramires Pires, additional, Filho, José Bione de Melo, additional, Santos, Alex Álisson Bandeira, additional, Ramos, Diogo Nunes da Silva, additional, and Moreira, Davidson Martins, additional

Published
2023
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4. EVALUATION OF THE PHYSICAL SENSITIVITY OF THE WRF-HYDRO MODEL IN THE SIMULATION OF RAINFALL IN MANAUS-AM USING DIFFERENT SETS OF PARAMETRIZATIONS

Author

Vieira, Carolina Sacramento, additional, Silva, Daniel Guimarães, additional, Silva Junior, Jose Roberto Dantas da, additional, Souza, Filipe Milani de, additional, Pedruzzi, Rizzieri, additional, Kitagawa, Yasmin Kaore Lago, additional, Ramos, Diogo Nunes da Silva, additional, and Moreira, Davidson Martins, additional

Published
2022
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15. Medium-term forecasting of global horizontal solar radiation in Brazil using machine learning-based methods.

Author

Weyll, Arthur Lúcide Cotta, Kitagawa, Yasmin Kaore Lago, Araujo, Mirella Lima Saraiva, Ramos, Diogo Nunes da Silva, Lima, Francisco José Lopes de, Santos, Thalyta Soares dos, Jacondino, William Duarte, Silva, Allan Rodrigues, Araújo, Allan Cavalcante, Pereira, Luana Kruger Melgaço, Pedruzzi, Rizzieri, de Carvalho Filho, Márcio, Bione de Melo Filho, José, Bandeira Santos, Alex Alisson, and Moreira, Davidson Martins

Subjects
*SOLAR radiation, *DOWNSCALING (Climatology), *GENERAL circulation model, *RENEWABLE energy sources, *CLIMATE change, *SOLAR technology, *MACHINE learning
Abstract

The generation of electric energy through renewable sources, such as solar photovoltaic (PV) systems, has emerged as one solution to the climate change crisis. To avoid fluctuations in energy generation on the electricity grid, reliable and accurate forecasts are required. Statistical downscaling methodologies often rely on data from General Circulation Model (GCM) to provide information on the local level. In contrast to traditional downscaling approaches, the present study focuses on a data-driven approach using the Global Data Assimilation System (GDAS) database in three different ways to feed the machine learning (ML) models, based on boosting and bagging, to provide medium-term Global Horizontal Irradiance (GHI) forecasts, i.e. up to seven days ahead. In addition, feature selection and feature augmentation techniques are also applied. The results showed that Extra Trees method outperformed the other tested in the present study, with a RMSE of 99.6 and 137.6 W/m2, and correlation 0.31 and 0.52 for June and October, respectively. Thus far, this work marks the initial suggestion of incorporating GDAS analysis data into a data-driven forecasting approach employing bagging and boosting models. This innovative approach demonstrates the potential for integrating global-scale data assimilation systems with local measurements using ML techniques. The outcomes of this research contribute to advancing the field of traditional downscaling methods and provides more robust and specialized forecasting. • Innovative ML-based solar irradiance forecasting approach using global weather data assimilation. • Statistical downscaling for medium-term GHI forecast extraction. • Methodology for limited data situations with 6-h-ahead forecasts. • Weather classification and transformation were used to preprocessing data to ML. • Comparative analysis with the competitiveness of the models against short-term forecasts using GDAS data. [ABSTRACT FROM AUTHOR]

Published
2024
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