A Wind Speed Interval Prediction Framework Based on Machine Learning Models and Kernel Density Estimation Method

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dc.authorscopusidİlhami Çolak / 6602990030
dc.authorwosidİlhami Çolak / KGT-0825-2024
dc.contributor.authorAl Hajj, Rami
dc.contributor.authorOskrochi, GholamReza
dc.contributor.authorAssi, Ali
dc.contributor.authorFouad, Mohamad M.
dc.contributor.authorÇolak, İlhami
dc.date.accessioned2025-04-18T09:47:16Z
dc.date.available2025-04-18T09:47:16Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractPredicting wind speed is crucial in the wind energy sector, especially for controlling and balancing production and consumption in smart grids. However, wind speed is characterized by high uncertainty and variability. These fluctuations in wind speed are obvious in short term timescales. Deterministic wind speed forecasts, also named point predictions, do not consider the inherent uncertainties in wind speed predictions. Estimating these uncertainties is essential for providing reliable information to energy operators, enabling them to develop effective operational strategies. In this work, we introduce a probabilistic prediction intervals approach for short-term wind speed forecasting. The proposed framework consists of a hybrid model that integrates machine learning models and an automatic feature selection technique to estimate both point predictions and prediction intervals for wind speed. The simulation works demonstrate the effectiveness of our designed framework and demonstrate its ability to generate satisfactory prediction intervals in the most adopted evaluation criteria. © 2024 IEEE.
dc.identifier.citationAl-Hajj, R., Oskrochi, G., Assi, A., Fouad, M. M., & Colak, I. (2024, November). A Wind Speed Interval Prediction Framework Based on Machine Learning Models and Kernel Density Estimation Method. In 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 998-1003). IEEE.
dc.identifier.doi10.1109/ICRERA62673.2024.10815465
dc.identifier.endpage1003
dc.identifier.isbn979-835037558-9
dc.identifier.issn2377-6897
dc.identifier.startpage998
dc.identifier.urihttp://dx.doi.org/10.1109/ICRERA62673.2024.10815465
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6861
dc.identifier.wosWOS:001416088100159
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÇolak, İlhami
dc.institutionauthoridİlhami Çolak / 0000-0002-6405-5938
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof13th International Conference on Renewable Energy Research and Applications, ICRERA 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectKernel Density Estimator
dc.subjectMachine Learning
dc.subjectPrediction Intervals
dc.subjectProbability Density Function
dc.subjectSmart Grid
dc.subjectWind Speed Prediction
dc.titleA Wind Speed Interval Prediction Framework Based on Machine Learning Models and Kernel Density Estimation Method
dc.typeConference Object

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