A Wind Speed Interval Prediction Framework Based on Machine Learning Models and Kernel Density Estimation Method
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Predicting 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.
Açıklama
Anahtar Kelimeler
Kernel Density Estimator, Machine Learning, Prediction Intervals, Probability Density Function, Smart Grid, Wind Speed Prediction
Kaynak
13th International Conference on Renewable Energy Research and Applications, ICRERA 2024
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Al-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.
