Predicting wind power using LSTM, Transformer, and other techniques
Predicting wind turbine energy is essential for optimizing renewable energy utilization and ensuring grid stability. Accurate forecasts enable effective resource planning, minimizing reliance on non-renewable energy sources and reducing carbon emissions. Additionally, precise predictions support eff...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | Clean Technologies and Recycling |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/ctr.2024007 |
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| author | Arun Kumar M Rithick Joshua K Sahana Rajesh Caroline Dorathy Esther J Kavitha Devi MK |
| author_facet | Arun Kumar M Rithick Joshua K Sahana Rajesh Caroline Dorathy Esther J Kavitha Devi MK |
| author_sort | Arun Kumar M |
| collection | DOAJ |
| description | Predicting wind turbine energy is essential for optimizing renewable energy utilization and ensuring grid stability. Accurate forecasts enable effective resource planning, minimizing reliance on non-renewable energy sources and reducing carbon emissions. Additionally, precise predictions support efficient grid management, allowing utilities to balance supply and demand in real time, ultimately enhancing energy reliability and sustainability. In this study, we bridge the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. We emphasize the importance of accuracy in these predictions, aiming to overcome current standards. Our approach utilized these models to predict wind power generation for the next 15 days, utilizing the SCADA Turkey dataset and Tata Power Poolavadi Data collected. We used R2 scores alongside traditional metrics like mean absolute error (MAE) and root mean square error (RMSE) to evaluate model performance. By employing these methodologies, we aim to enhance wind power forecasting, thereby enabling more efficient utilization of renewable energy resources. |
| format | Article |
| id | doaj-art-86f85257e86e4f4c93a4d862272c7e27 |
| institution | Kabale University |
| issn | 2770-4580 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Clean Technologies and Recycling |
| spelling | doaj-art-86f85257e86e4f4c93a4d862272c7e272025-01-23T07:56:47ZengAIMS PressClean Technologies and Recycling2770-45802024-12-014212514510.3934/ctr.2024007Predicting wind power using LSTM, Transformer, and other techniquesArun Kumar M0Rithick Joshua K1Sahana Rajesh2Caroline Dorathy Esther J3Kavitha Devi MK4Department of Computer Science and Engineering, Thiagarjar College of Engineering, Madurai-625015, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Thiagarjar College of Engineering, Madurai-625015, Tamil Nadu, IndiaDepartment of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore - 632014, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Thiagarjar College of Engineering, Madurai-625015, Tamil Nadu, IndiaFaculty of Computer Science and Engineering, Thiagarjar College of Engineering, Madurai-625015, Tamil Nadu, IndiaPredicting wind turbine energy is essential for optimizing renewable energy utilization and ensuring grid stability. Accurate forecasts enable effective resource planning, minimizing reliance on non-renewable energy sources and reducing carbon emissions. Additionally, precise predictions support efficient grid management, allowing utilities to balance supply and demand in real time, ultimately enhancing energy reliability and sustainability. In this study, we bridge the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. We emphasize the importance of accuracy in these predictions, aiming to overcome current standards. Our approach utilized these models to predict wind power generation for the next 15 days, utilizing the SCADA Turkey dataset and Tata Power Poolavadi Data collected. We used R2 scores alongside traditional metrics like mean absolute error (MAE) and root mean square error (RMSE) to evaluate model performance. By employing these methodologies, we aim to enhance wind power forecasting, thereby enabling more efficient utilization of renewable energy resources.https://www.aimspress.com/article/doi/10.3934/ctr.2024007temporal dependenciesfeature engineeringensemble methodsgrid integrationhyperparameter tuning |
| spellingShingle | Arun Kumar M Rithick Joshua K Sahana Rajesh Caroline Dorathy Esther J Kavitha Devi MK Predicting wind power using LSTM, Transformer, and other techniques Clean Technologies and Recycling temporal dependencies feature engineering ensemble methods grid integration hyperparameter tuning |
| title | Predicting wind power using LSTM, Transformer, and other techniques |
| title_full | Predicting wind power using LSTM, Transformer, and other techniques |
| title_fullStr | Predicting wind power using LSTM, Transformer, and other techniques |
| title_full_unstemmed | Predicting wind power using LSTM, Transformer, and other techniques |
| title_short | Predicting wind power using LSTM, Transformer, and other techniques |
| title_sort | predicting wind power using lstm transformer and other techniques |
| topic | temporal dependencies feature engineering ensemble methods grid integration hyperparameter tuning |
| url | https://www.aimspress.com/article/doi/10.3934/ctr.2024007 |
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