Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer
ABSTRACT Forecasting green energy is crucial in diminishing dependence on fossil fuels and fostering sustainable development. However, it encounters notable challenges, such as variable demand, restricted data availability, the integration of various datasets, and the necessity for precise long‐term...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.2091 |
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| author | Muhammad Shoaib Saleem Javed Rashid Sajjad Ahmad Ali M. Al‐Shaery Saad Althobaiti Muhammad Faheem |
| author_facet | Muhammad Shoaib Saleem Javed Rashid Sajjad Ahmad Ali M. Al‐Shaery Saad Althobaiti Muhammad Faheem |
| author_sort | Muhammad Shoaib Saleem |
| collection | DOAJ |
| description | ABSTRACT Forecasting green energy is crucial in diminishing dependence on fossil fuels and fostering sustainable development. However, it encounters notable challenges, such as variable demand, restricted data availability, the integration of various datasets, and the necessity for precise long‐term projections. This study thoughtfully examines these issues using the temporal fusion transformer (TFT) model to project green energy production across five Latin American nations (Argentina, Brazil, Chile, Colombia, and Mexico) and Canada, drawing on data from 1965 to 2023. The performance of the proposed TFT is more authentic as compared with the gated recurrent unit (GRU), the long short‐term memory (LSTM), deep autoregression (DeepAR), and the meta graph‐based convolutional recurrent network (MegaCRN). The TFT has a mean square error (MSE) of 0.0003, root mean square error (RMSE) of 0.0173, mean absolute error (MAE) of 0.0112 and mean absolute percentage error (MAPE) of 1.76%. From the preceding results, it is clear that the proposed TFT model can identify dynamic energy patterns that will contribute towards achieving sustainable development goals by the end of 2040. |
| format | Article |
| id | doaj-art-c9fd7303490040a5b4e5e03994b8c48e |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-c9fd7303490040a5b4e5e03994b8c48e2025-08-20T03:48:51ZengWileyEnergy Science & Engineering2050-05052025-05-011352262228310.1002/ese3.2091Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion TransformerMuhammad Shoaib Saleem0Javed Rashid1Sajjad Ahmad2Ali M. Al‐Shaery3Saad Althobaiti4Muhammad Faheem5Department of Mathematics University of Okara Okara PakistanMLC Research Lab, Okara Okara PakistanDepartment of Mathematics University of Okara Okara PakistanDepartment of Civil Engineering, College of Engineering and Architecture Umm Al‐Qura University Makkah Saudi ArabiaDepartment of Science and Technology, University College Ranyah Taif University Ranyah Saudi ArabiaSchool of Technology and Innovations University of Vaasa Vaasa FinlandABSTRACT Forecasting green energy is crucial in diminishing dependence on fossil fuels and fostering sustainable development. However, it encounters notable challenges, such as variable demand, restricted data availability, the integration of various datasets, and the necessity for precise long‐term projections. This study thoughtfully examines these issues using the temporal fusion transformer (TFT) model to project green energy production across five Latin American nations (Argentina, Brazil, Chile, Colombia, and Mexico) and Canada, drawing on data from 1965 to 2023. The performance of the proposed TFT is more authentic as compared with the gated recurrent unit (GRU), the long short‐term memory (LSTM), deep autoregression (DeepAR), and the meta graph‐based convolutional recurrent network (MegaCRN). The TFT has a mean square error (MSE) of 0.0003, root mean square error (RMSE) of 0.0173, mean absolute error (MAE) of 0.0112 and mean absolute percentage error (MAPE) of 1.76%. From the preceding results, it is clear that the proposed TFT model can identify dynamic energy patterns that will contribute towards achieving sustainable development goals by the end of 2040.https://doi.org/10.1002/ese3.2091deep autoregression (DeepAR)deep learning (DL)electricity predictiongated recurrent units (GRUs)green electrical productionlong‐term projections |
| spellingShingle | Muhammad Shoaib Saleem Javed Rashid Sajjad Ahmad Ali M. Al‐Shaery Saad Althobaiti Muhammad Faheem Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer Energy Science & Engineering deep autoregression (DeepAR) deep learning (DL) electricity prediction gated recurrent units (GRUs) green electrical production long‐term projections |
| title | Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer |
| title_full | Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer |
| title_fullStr | Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer |
| title_full_unstemmed | Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer |
| title_short | Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer |
| title_sort | forecasting green energy production in latin american countries and canada via temporal fusion transformer |
| topic | deep autoregression (DeepAR) deep learning (DL) electricity prediction gated recurrent units (GRUs) green electrical production long‐term projections |
| url | https://doi.org/10.1002/ese3.2091 |
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