Machine Learning Approaches for Short-Range Wind Power Estimation - A Perspective

Aug 4, 2026

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Wind energy production for near and offshore farms has accelerated through novel technologies that reduce overall economic costs. This paper reviews wind power estimation research with emphasis on innovative approaches using satellite data and artificial intelligence. A Sentinel image–based two-phased method combines machine learning for wind speed forecasting using Sentinel-1 and Sentinel-2 for wind speed and bathymetry. It also highlights a hybrid GRNN–WOA model and NCAR system upgrades for short-term forecasting, uncertainty quantification, and extreme-event prediction such as icing, improving accuracy and efficiency.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Introduction

The Importance of Wind Energy and Challenges in Offshore Installations

The Role of Satellite and Remote Sensing Technologies

Integration of Machine Learning and Artificial Intelligence

Objectives and Scope of This Review

Machine Learning Approaches for Short-Range Wind Power Estimation: A Perspective Ahmed Saleh Mahdi Kazem Al-Samalek1, Sonu Lal 2, Dr.P. Sharmila3, Abootharmahmoodshakir4, Ankita Joshi5, and R. Vimala Devi6, *Department of Medical Laboratory Technics, Al-Zahrawi University College, Karbala, Iraq †Department of Electronics & Communication Engineering, IES College Of Technology, IES University,Bhopal, Madhya Pradesh 462044 India. ‡Assistant Professor,Department of S&H,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai-127 §The Islamic university, Najaf, Iraq **epartment of Management, Uttaranchal Institute of Management, Uttaranchal University, Dehradun-248007, India 6Assistant Professor,Department of BCA,K.S.Rangasamy College of Arts and Science (Autonomous), Tiruchengode. Mail Id: [vimalrmail@gmail.com](vimalrmail@gmail.com) Abstract.The evolution of wind energy production, especially in near and offshore farms, has seen significant advancements due to the integration of novel technologies and the reduction in economic costs. This paper reviews the work in the domain of wind power estimation, emphasizing the innovative approaches leveraging satellite data and artificial intelligence (AI) methodologies. A notable method integrates Sentinel satellite imagery analysis in a two-phased approach, combined with machine learning techniques, to forecast wind speed. This method utilizes sentinel-1 and sentinel-2 satellite images for wind speed and bathymetry analysis, respectively. Furthermore, a hybrid forecasting model, comprising the generalized regression neural network (GRNN) and the whale optimization algorithm (WOA), has been introduced. Another pivotal advancement comes from the National Center for Atmospheric Research (NCAR), which has revamped its wind power forecasting system. This enhancement focuses on short-term forecasting, uncertainty quantification in wind speed prediction, and the prediction of extreme events like icing. The integration of numerical weather prediction with machine-learning methods, such asthe fuzzy logic artificial intelligence system, has further elevated the accuracy and efficiency of these forecasting models. Collectively, these *[Corresponding Authour :](Corresponding Authour :ahmeds909091@gmail.com)[ahmeds909091@gmail.com](Corresponding Authour :ahmeds909091@gmail.com) †[research@iesbpl.ac.in](research@iesbpl.ac.in) ‡[p](p.sharmila_phy@psvpec.in)[.sharmila_phy@psvpec.in](p.sharmila_phy@psvpec.in) § [abathermahmood560@gmail.com](abathermahmood560@gmail.com) **[Ankitajoshi2010@yahoo.com](Ankitajoshi2010@yahoo.com) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). advancements offer a comprehensive perspective on the future of short range wind power estimation. 1 Introduction The Importance of Wind Energy and Challenges in Offshore Installations Wind energy has emerged as a pivotal renewable energy source, gaining traction both onshore and offshore. While offshore wind energy production is generally more expensive than its onshore counterpart, it offers unique advantages. These include more stable and robust sea winds, lower environmental impact, and the potential for energy sustainability, especially for small islands with limited land area for onshore installations. However, the initial step in offshore wind farm development involves accurately assessing wind resources, identifying optimal locations based on various parameters such as wind speed, water depth, and distance to the shoreline. Traditional methods for these assessments, including in-situ measurement tools like cup anemometers and buoys, are often limited in scope, expensive, and time-consuming. The Role of Satellite and Remote Sensing Technologies Synthetic Aperture Radar (SAR) satellite me

FAQs

What is the main focus of this review on short-range wind power estimation?

The review focuses on how Sentinel satellite imagery and machine learning techniques are combined for wind speed and wind power assessment, supported by case studies and system developments.

How does the Sentinel-based method improve wind speed forecasting?

It uses Sentinel-1 for wind speed and Sentinel-2 for bathymetry, applying a two-phased workflow together with machine learning to forecast wind speed.

What enhancements does the NCAR wind power forecasting system provide?

It targets short-term forecasting with uncertainty quantification and includes prediction of extreme events such as icing, integrating numerical weather prediction with machine-learning approaches.

Aug 4, 2026

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