Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models
Forecasting wind speed plays an increasingly essential role in the wind energy industry. However, wind speed is uncertain with high changeability and dependency on weather conditions. Variability of wind energy is directly influenced by the fluctuation and unpredictability of wind speed. Traditional...
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AIMS Press
2025-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2025002 |
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| author | Rami Al-Hajj Gholamreza Oskrochi Mohamad M. Fouad Ali Assi |
| author_facet | Rami Al-Hajj Gholamreza Oskrochi Mohamad M. Fouad Ali Assi |
| author_sort | Rami Al-Hajj |
| collection | DOAJ |
| description | Forecasting wind speed plays an increasingly essential role in the wind energy industry. However, wind speed is uncertain with high changeability and dependency on weather conditions. Variability of wind energy is directly influenced by the fluctuation and unpredictability of wind speed. Traditional wind speed prediction methods provide deterministic forecasting that fails to estimate the uncertainties associated with wind speed predictions. Modeling those uncertainties is important to provide reliable information when the uncertainty level increases. Models for estimating prediction intervals of wind speed do not differentiate between daytime and nighttime shifts, which can affect the performance of probabilistic wind speed forecasting. In this paper, we introduce a prediction framework for deterministic and probabilistic short-term wind speed forecasting. The designed framework incorporates independent machine learning (ML) models to estimate point and interval prediction of wind speed during the daytime and nighttime shifts, respectively. First, feature selection techniques were applied to maintain the most relevant parameters in the datasets of daytime and nighttime shifts, respectively. Second, support vector regressors (SVRs) were used to predict the wind speed 10 minutes ahead. After that, we incorporated the non-parametric kernel density estimation (KDE) method to statistically synthesize the wind speed prediction errors and estimate the prediction intervals (PI) with several confidence levels. The simulation results validated the effectiveness of our framework and demonstrated that it can generate prediction intervals that are satisfactory in all evaluation criteria. This verifies the validity and feasibility of the hypothesis of separating the daytime and nighttime data sets for these types of predictions. |
| format | Article |
| id | doaj-art-a927e598e03a4663be499a3e73ec7fdd |
| institution | OA Journals |
| issn | 1551-0018 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-a927e598e03a4663be499a3e73ec7fdd2025-08-20T02:26:03ZengAIMS PressMathematical Biosciences and Engineering1551-00182025-01-01221235110.3934/mbe.2025002Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning modelsRami Al-Hajj0Gholamreza Oskrochi1Mohamad M. Fouad2Ali Assi3College of Engineering and Technology, American University of the Middle East, KuwaitCollege of Engineering and Technology, American University of the Middle East, KuwaitFaculty of Engineering, Mansoura University, EgyptIndependent Researcher, SMIEEE-Renewable Energy, Quebec, CanadaForecasting wind speed plays an increasingly essential role in the wind energy industry. However, wind speed is uncertain with high changeability and dependency on weather conditions. Variability of wind energy is directly influenced by the fluctuation and unpredictability of wind speed. Traditional wind speed prediction methods provide deterministic forecasting that fails to estimate the uncertainties associated with wind speed predictions. Modeling those uncertainties is important to provide reliable information when the uncertainty level increases. Models for estimating prediction intervals of wind speed do not differentiate between daytime and nighttime shifts, which can affect the performance of probabilistic wind speed forecasting. In this paper, we introduce a prediction framework for deterministic and probabilistic short-term wind speed forecasting. The designed framework incorporates independent machine learning (ML) models to estimate point and interval prediction of wind speed during the daytime and nighttime shifts, respectively. First, feature selection techniques were applied to maintain the most relevant parameters in the datasets of daytime and nighttime shifts, respectively. Second, support vector regressors (SVRs) were used to predict the wind speed 10 minutes ahead. After that, we incorporated the non-parametric kernel density estimation (KDE) method to statistically synthesize the wind speed prediction errors and estimate the prediction intervals (PI) with several confidence levels. The simulation results validated the effectiveness of our framework and demonstrated that it can generate prediction intervals that are satisfactory in all evaluation criteria. This verifies the validity and feasibility of the hypothesis of separating the daytime and nighttime data sets for these types of predictions.https://www.aimspress.com/article/doi/10.3934/mbe.2025002wind speed forecastingwind energyfeatures selectionsupport vector regressorsprediction intervalsprobabilistic energy forecastingkernel density estimator. |
| spellingShingle | Rami Al-Hajj Gholamreza Oskrochi Mohamad M. Fouad Ali Assi Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models Mathematical Biosciences and Engineering wind speed forecasting wind energy features selection support vector regressors prediction intervals probabilistic energy forecasting kernel density estimator. |
| title | Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models |
| title_full | Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models |
| title_fullStr | Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models |
| title_full_unstemmed | Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models |
| title_short | Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models |
| title_sort | probabilistic prediction intervals of short term wind speed using selected features and time shift dependent machine learning models |
| topic | wind speed forecasting wind energy features selection support vector regressors prediction intervals probabilistic energy forecasting kernel density estimator. |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2025002 |
| work_keys_str_mv | AT ramialhajj probabilisticpredictionintervalsofshorttermwindspeedusingselectedfeaturesandtimeshiftdependentmachinelearningmodels AT gholamrezaoskrochi probabilisticpredictionintervalsofshorttermwindspeedusingselectedfeaturesandtimeshiftdependentmachinelearningmodels AT mohamadmfouad probabilisticpredictionintervalsofshorttermwindspeedusingselectedfeaturesandtimeshiftdependentmachinelearningmodels AT aliassi probabilisticpredictionintervalsofshorttermwindspeedusingselectedfeaturesandtimeshiftdependentmachinelearningmodels |