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Artificial Intelligence for Renewable Energy Systems
book

Artificial Intelligence for Renewable Energy Systems

by Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman
March 2022
Intermediate to advanced content levelIntermediate to advanced
272 pages
6h 54m
English
Wiley-Scrivener
Content preview from Artificial Intelligence for Renewable Energy Systems

6Deep Learning Algorithms for Wind Forecasting: An Overview

M. Lydia1* and G. Edwin Prem Kumar2

1Dept. of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India

2Dept. of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India

Abstract

India, having the fourth largest installed capacity of wind power, is poised to grow in leaps and bounds in renewable energy utilization. The stochastic nature of wind has been a constant challenge in integration of wind power to the grid. According to the National Institute of Wind Energy (NIWE), the estimated wind potential of India at 120 m above ground level is around 695 GW. In order to effectively tap this power and to enhance wind power penetration in the grid, it is imperative that efficient wind speed and wind power forecasting models are in place. Forecasting of wind power aids in effective grid operations, planning of economic dispatch, estimation of candidate sites for wind farms and in scheduling operation and maintenance of wind farms. Deep learning models for wind forecasting have recently challenged the conventionally used forecasting models in terms of their accuracy, robust nature, and ability to handle huge volumes of data at a much lower computational cost. An exhaustive review of all the deep learning models used for wind speed/power forecasting is reviewed in this chapter. The research challenges faced and future research directions are also presented. ...

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Publisher Resources

ISBN: 9781119761693Purchase Link