Yıl 2025,
Cilt: 9 Sayı: 2, 395 - 404, 30.08.2025
Abdullah Fatih Höcü
,
Gül Fatma Türker
Kaynakça
-
1. Lu, H., Zhang, L., Tian, C., Niu, T., & Wei, W., "Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition", Energy Conversion and Management, Vol. 323, Pages 119155, 2024.
-
2. Sulaiman, M. H., & Mustaffa, Z., "Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization", Cleaner Energy Systems, Vol. 9, Pages 100139, 2024.
-
3. Tian, L., & Wei, Z., "Integration of VMD and neuro-fuzzy systems for wind speed analysis", Energy Conversion and Management, Vol. 323, Pages 119155, 2025.
-
4. Mehmood, Z., & Wang, Z., "Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms", International Journal of Green Energy, Vol. 22, Issue 2, Pages 414–431, 2025.
-
5. Bashir, T., Wang, H., Tahir, M. F., & Zhang, Y., "Short-term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", Renewable Energy, Vol. 239, Pages 122055, 2025.
-
6. Gilbert, A., & Huang, L., "Hierarchical approaches for turbine-level wind power forecasting", Renewable Energy, Vol. 167, Pages 119155, 2020.
-
7. Shao, L., Huang, W., Liu, H., & Li, J., "Study of wind power prediction in ELM based on improved SSA", IEEJ Transactions on Electrical and Electronic Engineering, 2025.
-
8. AlShafeey, M., & Csaki, C., "Adaptive machine learning for forecasting in wind energy", Heliyon, Vol. 10, Pages e34807, 2024.
-
9. Ukoba, K., Odebiyi, A., & Oghenevwogaga, J., "Harnessing machine learning for sustainable futures: Advancements in renewable energy and climate change mitigation", Bulletin of the National Research Centre, Vol. 48, Pages 99, 2024.
-
10. Yang, M., Guo, Y., Huang, T., & Zhang, W., "Power prediction considering NWP wind speed error tolerability", Applied Energy, Vol. 377, Pages 124720, 2025.
-
11. Liu, Z., Guo, H., Zhang, Y., & Zuo, Z., "A comprehensive review of wind power prediction based on machine learning", Energies, Vol. 18, Issue 350, 2025.
-
12. Mokarram, M., et al., "Adaptability of forecasting models across geographies", Sustainable Energy Technologies and Assessments, Vol. 54, Pages 104070, 2025.
-
13. Chen, X., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z., "Machine learning methods in weather and climate applications: A survey", Applied Energy, Vol. 13, Pages 12019, 2021.
-
14. Ahmad, M. W., Reynolds, J., & Rezgui, Y., "Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees", Journal of Cleaner Production, Vol. 203, Pages 810-821, 2018.
-
15. Panda, S. K., & Mohanty, S. N., "Time series forecasting and modeling of food demand supply chain based on regressors analysis", IEEE Access, Vol. 11, Pages 42679-42700, 2023.
-
16. Velthoen, J., Dombry, C., Cai, J. J., & Engelke, S., "Gradient boosting for extreme quantile regression", Extremes, Vol. 26, Issue 4, Pages 639-667, 2023.
-
17. Geurts, P., Ernst, D., & Wehenkel, L., "Extremely randomized trees", Machine Learnig, Vol. 63, Pages 3-42, 2006.
-
18. He, H. J., Zhang, C., Bian, X., An, J., Wang, Y., Ou, X., & Kamruzzaman, M., "Improved prediction of vitamin C and reducing sugar content in sweetpotatoes using hyperspectral imaging and LARS-enhanced LASSO variable selection", Journal of Food Composition and Analysis, Vol. 132, Pages 106350, 2024.
-
19. Ranstam, J., & Cook, J. A., "LASSO regression", Journal of British Surgery, Vol. 105, Issue 10, Pages 1348-1348, 2018.
-
20. Diebold, F. X., & Shin, M., "Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives", International Journal of Forecasting, Vol. 35, Issue 4, Pages 1679-1691, 2019.
-
21. Nayak, J., Vakula, K., Dinesh, P., Naik, B., & Pelusi, D., "Intelligent food processing: Journey from artificial neural network to deep learning", Computer Science Review, Vol. 38, Pages 100297, 2020.
-
22. Khan, M. I. H., Sablani, S. S., Nayak, R., & Gu, Y., "Machine learning‐based modeling in food processing applications: State of the art", Comprehensive Reviews in Food Science and Food Safety, Vol. 21, Issue 2, Pages 1409-1438, 2022.
-
23. Chicco, D., Warrens, M. J., & Jurman, G., "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", PeerJ Computer Science, Vol. 7, Pages e623, 2021.
-
24. Abdel-Aty, A.-H., et al., "Boosting wind turbine performance with advanced smart power prediction", Alexandria Engineering Journal, Vol. 96, Pages 58–71, 2024.
WIND TURBINE POWER PREDICTION USING MACHINE LEARNING MODELS: A CASE STUDY WITH REAL FARM DATA
Yıl 2025,
Cilt: 9 Sayı: 2, 395 - 404, 30.08.2025
Abdullah Fatih Höcü
,
Gül Fatma Türker
Öz
The power generated from wind turbines is of critical importance as one of the fundamental components of sustainable and renewable energy systems. However, the complex and nonlinear nature of wind flow and the influence of interconnected factors make turbine power estimation significantly difficult. This study aims to evaluate the performance of different forecasting models using real-time data obtained from wind turbines and to determine the most effective model for wind power generation. The analyses are performed based on performance metrics that measure the agreement between the predicted and actual values. The study results reveal that the Decision Tree Regressor model provides the highest accuracy with 0.998 R² value and low error rates (RMSE: 0.151, MAE: 0.036) and that tree-based models are more effective in wind power estimation. These models, trained using large datasets, offer significant potential in terms of increasing power grid stability and ensuring the optimization of wind farms. The study shows that advanced methods used in turbine power estimation are an effective tool for optimizing renewable energy use by contributing to sustainable energy targets.
Kaynakça
-
1. Lu, H., Zhang, L., Tian, C., Niu, T., & Wei, W., "Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition", Energy Conversion and Management, Vol. 323, Pages 119155, 2024.
-
2. Sulaiman, M. H., & Mustaffa, Z., "Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization", Cleaner Energy Systems, Vol. 9, Pages 100139, 2024.
-
3. Tian, L., & Wei, Z., "Integration of VMD and neuro-fuzzy systems for wind speed analysis", Energy Conversion and Management, Vol. 323, Pages 119155, 2025.
-
4. Mehmood, Z., & Wang, Z., "Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms", International Journal of Green Energy, Vol. 22, Issue 2, Pages 414–431, 2025.
-
5. Bashir, T., Wang, H., Tahir, M. F., & Zhang, Y., "Short-term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", Renewable Energy, Vol. 239, Pages 122055, 2025.
-
6. Gilbert, A., & Huang, L., "Hierarchical approaches for turbine-level wind power forecasting", Renewable Energy, Vol. 167, Pages 119155, 2020.
-
7. Shao, L., Huang, W., Liu, H., & Li, J., "Study of wind power prediction in ELM based on improved SSA", IEEJ Transactions on Electrical and Electronic Engineering, 2025.
-
8. AlShafeey, M., & Csaki, C., "Adaptive machine learning for forecasting in wind energy", Heliyon, Vol. 10, Pages e34807, 2024.
-
9. Ukoba, K., Odebiyi, A., & Oghenevwogaga, J., "Harnessing machine learning for sustainable futures: Advancements in renewable energy and climate change mitigation", Bulletin of the National Research Centre, Vol. 48, Pages 99, 2024.
-
10. Yang, M., Guo, Y., Huang, T., & Zhang, W., "Power prediction considering NWP wind speed error tolerability", Applied Energy, Vol. 377, Pages 124720, 2025.
-
11. Liu, Z., Guo, H., Zhang, Y., & Zuo, Z., "A comprehensive review of wind power prediction based on machine learning", Energies, Vol. 18, Issue 350, 2025.
-
12. Mokarram, M., et al., "Adaptability of forecasting models across geographies", Sustainable Energy Technologies and Assessments, Vol. 54, Pages 104070, 2025.
-
13. Chen, X., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z., "Machine learning methods in weather and climate applications: A survey", Applied Energy, Vol. 13, Pages 12019, 2021.
-
14. Ahmad, M. W., Reynolds, J., & Rezgui, Y., "Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees", Journal of Cleaner Production, Vol. 203, Pages 810-821, 2018.
-
15. Panda, S. K., & Mohanty, S. N., "Time series forecasting and modeling of food demand supply chain based on regressors analysis", IEEE Access, Vol. 11, Pages 42679-42700, 2023.
-
16. Velthoen, J., Dombry, C., Cai, J. J., & Engelke, S., "Gradient boosting for extreme quantile regression", Extremes, Vol. 26, Issue 4, Pages 639-667, 2023.
-
17. Geurts, P., Ernst, D., & Wehenkel, L., "Extremely randomized trees", Machine Learnig, Vol. 63, Pages 3-42, 2006.
-
18. He, H. J., Zhang, C., Bian, X., An, J., Wang, Y., Ou, X., & Kamruzzaman, M., "Improved prediction of vitamin C and reducing sugar content in sweetpotatoes using hyperspectral imaging and LARS-enhanced LASSO variable selection", Journal of Food Composition and Analysis, Vol. 132, Pages 106350, 2024.
-
19. Ranstam, J., & Cook, J. A., "LASSO regression", Journal of British Surgery, Vol. 105, Issue 10, Pages 1348-1348, 2018.
-
20. Diebold, F. X., & Shin, M., "Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives", International Journal of Forecasting, Vol. 35, Issue 4, Pages 1679-1691, 2019.
-
21. Nayak, J., Vakula, K., Dinesh, P., Naik, B., & Pelusi, D., "Intelligent food processing: Journey from artificial neural network to deep learning", Computer Science Review, Vol. 38, Pages 100297, 2020.
-
22. Khan, M. I. H., Sablani, S. S., Nayak, R., & Gu, Y., "Machine learning‐based modeling in food processing applications: State of the art", Comprehensive Reviews in Food Science and Food Safety, Vol. 21, Issue 2, Pages 1409-1438, 2022.
-
23. Chicco, D., Warrens, M. J., & Jurman, G., "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", PeerJ Computer Science, Vol. 7, Pages e623, 2021.
-
24. Abdel-Aty, A.-H., et al., "Boosting wind turbine performance with advanced smart power prediction", Alexandria Engineering Journal, Vol. 96, Pages 58–71, 2024.
WIND TURBINE POWER PREDICTION USING MACHINE LEARNING MODELS: A CASE STUDY WITH REAL FARM DATA
Yıl 2025,
Cilt: 9 Sayı: 2, 395 - 404, 30.08.2025
Abdullah Fatih Höcü
,
Gül Fatma Türker
Öz
The power generated from wind turbines is of critical importance as one of the fundamental components of sustainable and renewable energy systems. However, the complex and nonlinear nature of wind flow and the influence of interconnected factors make turbine power estimation significantly difficult. This study aims to evaluate the performance of different forecasting models using real-time data obtained from wind turbines and to determine the most effective model for wind power generation. The analyses are performed based on performance metrics that measure the agreement between the predicted and actual values. The study results reveal that the Decision Tree Regressor model provides the highest accuracy with 0.998 R² value and low error rates (RMSE: 0.151, MAE: 0.036) and that tree-based models are more effective in wind power estimation. These models, trained using large datasets, offer significant potential in terms of increasing power grid stability and ensuring the optimization of wind farms. The study shows that advanced methods used in turbine power estimation are an effective tool for optimizing renewable energy use by contributing to sustainable energy targets.
Kaynakça
-
1. Lu, H., Zhang, L., Tian, C., Niu, T., & Wei, W., "Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition", Energy Conversion and Management, Vol. 323, Pages 119155, 2024.
-
2. Sulaiman, M. H., & Mustaffa, Z., "Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization", Cleaner Energy Systems, Vol. 9, Pages 100139, 2024.
-
3. Tian, L., & Wei, Z., "Integration of VMD and neuro-fuzzy systems for wind speed analysis", Energy Conversion and Management, Vol. 323, Pages 119155, 2025.
-
4. Mehmood, Z., & Wang, Z., "Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms", International Journal of Green Energy, Vol. 22, Issue 2, Pages 414–431, 2025.
-
5. Bashir, T., Wang, H., Tahir, M. F., & Zhang, Y., "Short-term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", Renewable Energy, Vol. 239, Pages 122055, 2025.
-
6. Gilbert, A., & Huang, L., "Hierarchical approaches for turbine-level wind power forecasting", Renewable Energy, Vol. 167, Pages 119155, 2020.
-
7. Shao, L., Huang, W., Liu, H., & Li, J., "Study of wind power prediction in ELM based on improved SSA", IEEJ Transactions on Electrical and Electronic Engineering, 2025.
-
8. AlShafeey, M., & Csaki, C., "Adaptive machine learning for forecasting in wind energy", Heliyon, Vol. 10, Pages e34807, 2024.
-
9. Ukoba, K., Odebiyi, A., & Oghenevwogaga, J., "Harnessing machine learning for sustainable futures: Advancements in renewable energy and climate change mitigation", Bulletin of the National Research Centre, Vol. 48, Pages 99, 2024.
-
10. Yang, M., Guo, Y., Huang, T., & Zhang, W., "Power prediction considering NWP wind speed error tolerability", Applied Energy, Vol. 377, Pages 124720, 2025.
-
11. Liu, Z., Guo, H., Zhang, Y., & Zuo, Z., "A comprehensive review of wind power prediction based on machine learning", Energies, Vol. 18, Issue 350, 2025.
-
12. Mokarram, M., et al., "Adaptability of forecasting models across geographies", Sustainable Energy Technologies and Assessments, Vol. 54, Pages 104070, 2025.
-
13. Chen, X., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z., "Machine learning methods in weather and climate applications: A survey", Applied Energy, Vol. 13, Pages 12019, 2021.
-
14. Ahmad, M. W., Reynolds, J., & Rezgui, Y., "Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees", Journal of Cleaner Production, Vol. 203, Pages 810-821, 2018.
-
15. Panda, S. K., & Mohanty, S. N., "Time series forecasting and modeling of food demand supply chain based on regressors analysis", IEEE Access, Vol. 11, Pages 42679-42700, 2023.
-
16. Velthoen, J., Dombry, C., Cai, J. J., & Engelke, S., "Gradient boosting for extreme quantile regression", Extremes, Vol. 26, Issue 4, Pages 639-667, 2023.
-
17. Geurts, P., Ernst, D., & Wehenkel, L., "Extremely randomized trees", Machine Learnig, Vol. 63, Pages 3-42, 2006.
-
18. He, H. J., Zhang, C., Bian, X., An, J., Wang, Y., Ou, X., & Kamruzzaman, M., "Improved prediction of vitamin C and reducing sugar content in sweetpotatoes using hyperspectral imaging and LARS-enhanced LASSO variable selection", Journal of Food Composition and Analysis, Vol. 132, Pages 106350, 2024.
-
19. Ranstam, J., & Cook, J. A., "LASSO regression", Journal of British Surgery, Vol. 105, Issue 10, Pages 1348-1348, 2018.
-
20. Diebold, F. X., & Shin, M., "Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives", International Journal of Forecasting, Vol. 35, Issue 4, Pages 1679-1691, 2019.
-
21. Nayak, J., Vakula, K., Dinesh, P., Naik, B., & Pelusi, D., "Intelligent food processing: Journey from artificial neural network to deep learning", Computer Science Review, Vol. 38, Pages 100297, 2020.
-
22. Khan, M. I. H., Sablani, S. S., Nayak, R., & Gu, Y., "Machine learning‐based modeling in food processing applications: State of the art", Comprehensive Reviews in Food Science and Food Safety, Vol. 21, Issue 2, Pages 1409-1438, 2022.
-
23. Chicco, D., Warrens, M. J., & Jurman, G., "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation", PeerJ Computer Science, Vol. 7, Pages e623, 2021.
-
24. Abdel-Aty, A.-H., et al., "Boosting wind turbine performance with advanced smart power prediction", Alexandria Engineering Journal, Vol. 96, Pages 58–71, 2024.