A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

K. R. Sri Preethaa, Akila Muthuramalingam, Yuvaraj Natarajan, Gitanjali Wadhwa and Ahmed Abdi Yusuf Ali ()
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K. R. Sri Preethaa: Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India
Akila Muthuramalingam: Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India
Yuvaraj Natarajan: Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India
Gitanjali Wadhwa: Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, India
Ahmed Abdi Yusuf Ali: Department of Electrical Engineering, University of Johannesburg, Johannesburg 2092, South Africa

Sustainability, 2023, vol. 15, issue 17, 1-22

Abstract: The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-based sectors. Several atmospheric and geographical factors influence wind characteristics. Many wind forecasting methods and tools have been introduced since early times. Wind forecasting can be carried out short-, medium-, and long-term. The uncertainty factors of the wind challenge the accuracy of techniques. This article brings the general background of physical, statistical, and intelligent approaches and their methods used to predict wind characteristics and their challenges—this work’s objective is to improve effective data-driven models for forecasting wind-power production. The investigation and listing of the effectiveness of improved machine learning models to estimate univariate wind-energy time-based data is crucially the prominent focus of this work. The performance of various ML predicting models was examined using ensemble learning (ES) models, such as boosted trees and bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks have recently been constructed for forecasting wind speed and power due to artificial intelligence (AI) advancement. Based on the model summary, further directions for research and application developments can be planned.

Keywords: wind pattern forecasting; machine learning; ensemble learning; deep learning hybrid model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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