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Related Experiment Video

Updated: May 28, 2025

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine

Rami Al-Hajj¹, Gholamreza Oskrochi¹, Mohamad M Fouad²

¹College of Engineering and Technology, American University of the Middle East, Kuwait.

Mathematical Biosciences and Engineering : MBE

|February 14, 2025

Summary

This study introduces a new framework for forecasting wind speed, improving accuracy by separating daytime and nighttime predictions. This approach enhances reliability for the wind energy industry by better modeling wind speed uncertainties.

Keywords:

features selection kernel density estimator prediction intervals probabilistic energy forecasting support vector regressors wind energy wind speed forecasting

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Area of Science:

Renewable Energy Systems
Meteorological Forecasting
Machine Learning Applications

Background:

Wind speed forecasting is crucial for the wind energy industry but faces challenges due to high variability and unpredictability.
Traditional deterministic methods fail to capture wind speed uncertainties, impacting the reliability of wind energy predictions.
Existing prediction interval models do not account for diurnal variations, potentially affecting probabilistic forecasting performance.

Purpose of the Study:

To develop a novel framework for both deterministic and probabilistic short-term wind speed forecasting.
To improve the accuracy and reliability of wind speed predictions by incorporating daytime and nighttime specific models.
To effectively model and communicate the uncertainties associated with wind speed forecasts.

Main Methods:

Applied feature selection to identify relevant parameters for distinct daytime and nighttime datasets.
Utilized Support Vector Regressors (SVRs) for 10-minute ahead wind speed point predictions.
Employed Kernel Density Estimation (KDE) for synthesizing prediction errors and estimating prediction intervals (PI) at various confidence levels.

Main Results:

The proposed framework demonstrated effectiveness in generating satisfactory prediction intervals across all evaluation criteria.
Simulation results validated the framework's ability to provide reliable deterministic and probabilistic wind speed forecasts.
The hypothesis of separating daytime and nighttime datasets for improved prediction accuracy was confirmed.

Conclusions:

The developed framework offers a significant advancement in short-term wind speed forecasting for the wind energy sector.
Separating data based on daytime and nighttime shifts enhances the performance of machine learning models for wind speed prediction.
The approach provides a feasible and effective method for estimating prediction intervals, crucial for managing wind energy uncertainties.