The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks
Wind power has become more popular due to an increase in energy demand and the rapid decline in conventional fossil fuels. This paper addresses the rising demand for accurate short-term wind power forecasting, which is critical for minimizing the impacts on grid operations and reducing associated co...
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| Format: | Article |
| Language: | English |
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Academy of Sciences of Moldova
2025-02-01
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| Series: | Problems of the Regional Energetics |
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| Online Access: | https://journal.ie.asm.md/assets/files/01_01_65_2025.pdf |
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| author | Sunku V.S. Namboodiri V. Mukkamala R. |
| author_facet | Sunku V.S. Namboodiri V. Mukkamala R. |
| author_sort | Sunku V.S. |
| collection | DOAJ |
| description | Wind power has become more popular due to an increase in energy demand and the rapid decline in conventional fossil fuels. This paper addresses the rising demand for accurate short-term wind power forecasting, which is critical for minimizing the impacts on grid operations and reducing associated costs. The objective is to develop an innovative deep learning (DL) model that integrates a convolutional neural network (CNN) with a gated recurrent unit (GRU) to enhance forecasting precision for day-ahead applications. In pursuit of these objectives, the CNN GRU model was rigorously tested and compared against three additional models: CNN with bidirectional long short-term memory (BiLSTM), extreme gradient boosting (XGBoost), and random forest (RF). Key performance metrics—namely, mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²)—were employed to assess the efficacy of each model. Statistical validation was also performed using the Diebold-Mariano test to establish significant differences in performance. The most important results reveal that the CNN GRU model outperformed the other models, achieving a MAE of 0.2104 MW, an MSE of 0.1028 MW, an RMSE of 0.3206 MW, and an R² of 0.9768. These findings underscore the model's superior accuracy and reliability in the realm of short-term wind power forecasting. The significance of this research resides in its demonstration of the CNN GRU model as an effective and practical instrument for renewable energy forecasting. |
| format | Article |
| id | doaj-art-3f08734320554553a691851182e7a6d1 |
| institution | Kabale University |
| issn | 1857-0070 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Academy of Sciences of Moldova |
| record_format | Article |
| series | Problems of the Regional Energetics |
| spelling | doaj-art-3f08734320554553a691851182e7a6d12025-02-06T07:56:35ZengAcademy of Sciences of MoldovaProblems of the Regional Energetics1857-00702025-02-0165111110.52254/1857-0070.2025.1-65.01The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning FrameworksSunku V.S.0Namboodiri V.1Mukkamala R. 2School of Energy & Clean Technology, NICMAR University of Construction Studies, Hyderabad, IndiaSchool of Energy & Clean Technology, NICMAR University of Construction Studies, Hyderabad, IndiaSchool of Energy & Clean Technology, NICMAR University of Construction Studies, Hyderabad, IndiaWind power has become more popular due to an increase in energy demand and the rapid decline in conventional fossil fuels. This paper addresses the rising demand for accurate short-term wind power forecasting, which is critical for minimizing the impacts on grid operations and reducing associated costs. The objective is to develop an innovative deep learning (DL) model that integrates a convolutional neural network (CNN) with a gated recurrent unit (GRU) to enhance forecasting precision for day-ahead applications. In pursuit of these objectives, the CNN GRU model was rigorously tested and compared against three additional models: CNN with bidirectional long short-term memory (BiLSTM), extreme gradient boosting (XGBoost), and random forest (RF). Key performance metrics—namely, mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²)—were employed to assess the efficacy of each model. Statistical validation was also performed using the Diebold-Mariano test to establish significant differences in performance. The most important results reveal that the CNN GRU model outperformed the other models, achieving a MAE of 0.2104 MW, an MSE of 0.1028 MW, an RMSE of 0.3206 MW, and an R² of 0.9768. These findings underscore the model's superior accuracy and reliability in the realm of short-term wind power forecasting. The significance of this research resides in its demonstration of the CNN GRU model as an effective and practical instrument for renewable energy forecasting. https://journal.ie.asm.md/assets/files/01_01_65_2025.pdfwind powerforecastingdeep learningrenewable energyperformance metrics. |
| spellingShingle | Sunku V.S. Namboodiri V. Mukkamala R. The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks Problems of the Regional Energetics wind power forecasting deep learning renewable energy performance metrics. |
| title | The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks |
| title_full | The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks |
| title_fullStr | The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks |
| title_full_unstemmed | The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks |
| title_short | The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks |
| title_sort | short term wind power forecasting by utilizing machine learning and hybrid deep learning frameworks |
| topic | wind power forecasting deep learning renewable energy performance metrics. |
| url | https://journal.ie.asm.md/assets/files/01_01_65_2025.pdf |
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