Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting
Wind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific comunity. This parameter is of interest to both researchers interested in climate change and researc...
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MDPI AG
2025-03-01
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| author | Türker Tuğrul Sertaç Oruç Mehmet Ali Hınıs |
| author_facet | Türker Tuğrul Sertaç Oruç Mehmet Ali Hınıs |
| author_sort | Türker Tuğrul |
| collection | DOAJ |
| description | Wind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific comunity. This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. In these analyses, five different models were created with the assistance of cross-correlation. The models obtained from the analyses were improved with the wavelet transformation (WT), and the results obtained were evaluated for the correlation coefficient (R), the Nash–Sutcliffe model efficiency (NSE), the Kling–Gupta efficiency (KGE), the performance index (PI), the root mean standard deviation ratio (RSR), and the root mean square error (RMSE). The results obtained from this study unveiled that LSTM emerged as the best performance metric in the M04 model among other models (R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248). Another notable finding obtained from this study was that the best performance values in analyses without WT were obtained with SARIMA. The results of this study provide information on forward-looking modeling for institutions and decision-makers related to energy and climate change. |
| format | Article |
| id | doaj-art-e1ccbcaeffcc482aa9db526ddf8cbac9 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e1ccbcaeffcc482aa9db526ddf8cbac92025-08-20T03:06:31ZengMDPI AGApplied Sciences2076-34172025-03-01157354310.3390/app15073543Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed ForecastingTürker Tuğrul0Sertaç Oruç1Mehmet Ali Hınıs2Research Development Institution Coordinator Office, Gazi University, Ankara 06560, TürkiyeFaculty of Engineering and Natural Sciences, Civil Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, TürkiyeFaculty of Engineering, Civil Engineering, Aksaray University, Aksaray 68100, TürkiyeWind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific comunity. This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. In these analyses, five different models were created with the assistance of cross-correlation. The models obtained from the analyses were improved with the wavelet transformation (WT), and the results obtained were evaluated for the correlation coefficient (R), the Nash–Sutcliffe model efficiency (NSE), the Kling–Gupta efficiency (KGE), the performance index (PI), the root mean standard deviation ratio (RSR), and the root mean square error (RMSE). The results obtained from this study unveiled that LSTM emerged as the best performance metric in the M04 model among other models (R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248). Another notable finding obtained from this study was that the best performance values in analyses without WT were obtained with SARIMA. The results of this study provide information on forward-looking modeling for institutions and decision-makers related to energy and climate change.https://www.mdpi.com/2076-3417/15/7/3543wind speedLSTMANNSVMwavelet transformmachine learning |
| spellingShingle | Türker Tuğrul Sertaç Oruç Mehmet Ali Hınıs Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting Applied Sciences wind speed LSTM ANN SVM wavelet transform machine learning |
| title | Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting |
| title_full | Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting |
| title_fullStr | Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting |
| title_full_unstemmed | Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting |
| title_short | Transforming Wind Data into Insights: A Comparative Study of Stochastic and Machine Learning Models in Wind Speed Forecasting |
| title_sort | transforming wind data into insights a comparative study of stochastic and machine learning models in wind speed forecasting |
| topic | wind speed LSTM ANN SVM wavelet transform machine learning |
| url | https://www.mdpi.com/2076-3417/15/7/3543 |
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