Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin
Forecasting models employing machine learning (ML) and deep learning (DL) have become fundamental for assessing the technical feasibility of renewable energy systems. Among these, solar energy stands out as a renewable energy option, particularly relevant for supporting the preservation of the Amazo...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11080416/ |
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| author | Andre Luis Ferreira Marques Ricardo Sbragio Pedro Luiz Pizzigatti Correa Marcelo Ramos Martins |
| author_facet | Andre Luis Ferreira Marques Ricardo Sbragio Pedro Luiz Pizzigatti Correa Marcelo Ramos Martins |
| author_sort | Andre Luis Ferreira Marques |
| collection | DOAJ |
| description | Forecasting models employing machine learning (ML) and deep learning (DL) have become fundamental for assessing the technical feasibility of renewable energy systems. Among these, solar energy stands out as a renewable energy option, particularly relevant for supporting the preservation of the Amazon rainforest. This study introduces a novel approach using ML and DL methods—integrated with Universal Kriging and Holt-Winters (time series) models — to forecast solar irradiance (kWh/m2) in cities across the state of Amazonas. The analysis is grounded in the Data Science cycle, with input data sourced from both ground stations and satellite products. Forecasting performance was evaluated for short-term horizons (one to three days ahead) across three representative cities. The hybrid SARIMAX-CNN-LSTM, SARIMAX-CNN-Transformer, and SARIMAX-TCN models achieved MAPE values ranging from 18.1% to 26.6% for the different forecast horizons and cities. These results are consistent with existing literature and reinforce the suitability of advanced ML/DL approaches for solar energy forecasting in highly variable and challenging environments such as the Amazon Basin. |
| format | Article |
| id | doaj-art-a67386dbb64b4501aed416b94e97f8e6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a67386dbb64b4501aed416b94e97f8e62025-08-20T03:55:48ZengIEEEIEEE Access2169-35362025-01-011312506612507910.1109/ACCESS.2025.358927511080416Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon BasinAndre Luis Ferreira Marques0https://orcid.org/0000-0003-4110-9398Ricardo Sbragio1Pedro Luiz Pizzigatti Correa2https://orcid.org/0000-0002-8743-4244Marcelo Ramos Martins3Computer Engineering Department, BigData Lab C216, Polytechnic School of the University of São Paulo, São Paulo, BrazilNaval Architecture and Ocean Engineering Department, Laboratory of Analysis, Evaluation and Risk Management (LabRisco), Polytechnic School of the University of São Paulo, São Paulo, BrazilComputer Engineering Department, BigData Lab C216, Polytechnic School of the University of São Paulo, São Paulo, BrazilNaval Architecture and Ocean Engineering Department, Laboratory of Analysis, Evaluation and Risk Management (LabRisco), Polytechnic School of the University of São Paulo, São Paulo, BrazilForecasting models employing machine learning (ML) and deep learning (DL) have become fundamental for assessing the technical feasibility of renewable energy systems. Among these, solar energy stands out as a renewable energy option, particularly relevant for supporting the preservation of the Amazon rainforest. This study introduces a novel approach using ML and DL methods—integrated with Universal Kriging and Holt-Winters (time series) models — to forecast solar irradiance (kWh/m2) in cities across the state of Amazonas. The analysis is grounded in the Data Science cycle, with input data sourced from both ground stations and satellite products. Forecasting performance was evaluated for short-term horizons (one to three days ahead) across three representative cities. The hybrid SARIMAX-CNN-LSTM, SARIMAX-CNN-Transformer, and SARIMAX-TCN models achieved MAPE values ranging from 18.1% to 26.6% for the different forecast horizons and cities. These results are consistent with existing literature and reinforce the suitability of advanced ML/DL approaches for solar energy forecasting in highly variable and challenging environments such as the Amazon Basin.https://ieeexplore.ieee.org/document/11080416/Deep learninglong short-term memorymulti-layer perceptrondata scienceAmazon Basinsolar energy |
| spellingShingle | Andre Luis Ferreira Marques Ricardo Sbragio Pedro Luiz Pizzigatti Correa Marcelo Ramos Martins Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin IEEE Access Deep learning long short-term memory multi-layer perceptron data science Amazon Basin solar energy |
| title | Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin |
| title_full | Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin |
| title_fullStr | Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin |
| title_full_unstemmed | Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin |
| title_short | Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin |
| title_sort | artificial intelligence in data science evaluating forecasting models for solar energy in the amazon basin |
| topic | Deep learning long short-term memory multi-layer perceptron data science Amazon Basin solar energy |
| url | https://ieeexplore.ieee.org/document/11080416/ |
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