Forecasting power generation of wind turbine with real-time data using machine learning algorithms
The escalating concern over the adverse effects of greenhouse gas emissions on the Earth's climate has intensified the need for sustainable and renewable energy sources. Among the alternatives, wind energy has emerged as a key solution for mitigating the impacts of global warming. The significa...
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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.2024006 |
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| author | Asiye Bilgili Kerem Gül |
| author_facet | Asiye Bilgili Kerem Gül |
| author_sort | Asiye Bilgili |
| collection | DOAJ |
| description | The escalating concern over the adverse effects of greenhouse gas emissions on the Earth's climate has intensified the need for sustainable and renewable energy sources. Among the alternatives, wind energy has emerged as a key solution for mitigating the impacts of global warming. The significance of wind energy generation lies in its abundance, environmental benefits, cost-effectiveness and contribution to energy security. Accurate forecasting of wind energy generation is crucial for managing its intermittent nature and ensuring effective integration into the electricity grid. We employed machine learning techniques to predict wind power generation by utilizing historical weather data in conjunction with corresponding wind power generation data. The dataset was sourced from real-time SCADA data obtained from wind turbines, allowing for a comprehensive analysis. We differentiated this research by evaluating not only wind conditions but also meteorological factors and physical measurements of turbine components, thus considering their combined influence on overall wind power production. We utilized Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost algorithms to estimate power generation. The performance of these models assessed using evaluation criteria: R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicated XGBoost algorithm outperformed the other models, achieving high accuracy while demonstrating computational efficiency, making it particularly suitable for real-time applications in energy forecasting. |
| format | Article |
| id | doaj-art-f5497cba806c4650996bf802df3dbc56 |
| 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-f5497cba806c4650996bf802df3dbc562025-01-23T07:56:46ZengAIMS PressClean Technologies and Recycling2770-45802024-12-014210812410.3934/ctr.2024006Forecasting power generation of wind turbine with real-time data using machine learning algorithmsAsiye Bilgili0Kerem Gül1Department of Informatics, Institute of Graduate Studies in Sciences, Istanbul University, Istanbul, TurkeyDepartment of Mechanical Engineering, Faculty of Engineering, Halic University, Istanbul, TurkeyThe escalating concern over the adverse effects of greenhouse gas emissions on the Earth's climate has intensified the need for sustainable and renewable energy sources. Among the alternatives, wind energy has emerged as a key solution for mitigating the impacts of global warming. The significance of wind energy generation lies in its abundance, environmental benefits, cost-effectiveness and contribution to energy security. Accurate forecasting of wind energy generation is crucial for managing its intermittent nature and ensuring effective integration into the electricity grid. We employed machine learning techniques to predict wind power generation by utilizing historical weather data in conjunction with corresponding wind power generation data. The dataset was sourced from real-time SCADA data obtained from wind turbines, allowing for a comprehensive analysis. We differentiated this research by evaluating not only wind conditions but also meteorological factors and physical measurements of turbine components, thus considering their combined influence on overall wind power production. We utilized Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost algorithms to estimate power generation. The performance of these models assessed using evaluation criteria: R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicated XGBoost algorithm outperformed the other models, achieving high accuracy while demonstrating computational efficiency, making it particularly suitable for real-time applications in energy forecasting.https://www.aimspress.com/article/doi/10.3934/ctr.2024006forecasting power productionglobal warmingrenewable energysustainable energy |
| spellingShingle | Asiye Bilgili Kerem Gül Forecasting power generation of wind turbine with real-time data using machine learning algorithms Clean Technologies and Recycling forecasting power production global warming renewable energy sustainable energy |
| title | Forecasting power generation of wind turbine with real-time data using machine learning algorithms |
| title_full | Forecasting power generation of wind turbine with real-time data using machine learning algorithms |
| title_fullStr | Forecasting power generation of wind turbine with real-time data using machine learning algorithms |
| title_full_unstemmed | Forecasting power generation of wind turbine with real-time data using machine learning algorithms |
| title_short | Forecasting power generation of wind turbine with real-time data using machine learning algorithms |
| title_sort | forecasting power generation of wind turbine with real time data using machine learning algorithms |
| topic | forecasting power production global warming renewable energy sustainable energy |
| url | https://www.aimspress.com/article/doi/10.3934/ctr.2024006 |
| work_keys_str_mv | AT asiyebilgili forecastingpowergenerationofwindturbinewithrealtimedatausingmachinelearningalgorithms AT keremgul forecastingpowergenerationofwindturbinewithrealtimedatausingmachinelearningalgorithms |