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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

8Deep Feature Selection for Wind Forecasting-II

S. Oswalt Manoj1*, J.P. Ananth2, Balan Dhanka3 and Maharaja Kamatchi4

1Department of Computer Science and Business System, Sri Krishna College of Engineering and Technology, Coimbatore, India

2Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India

3University of Rajasthan, Rajasthan, India

4Engineering Department, University of Technology and Applied Sciences, Al Mussanah, Oman

Abstract

In recent years, large production of energy from renewable sources is significantly getting boosted in every part of the world. Due to the expeditious development of the penetration of the power related to wind into the present day power grid, the forecasting of wind speed turns into an expanding noteworthy assignment in power generation process. Accordingly, the scope of the chapter is to determine the forecasting of the wind speed in short term by incorporating the adaptive ensembles of Deep Neural Network, and the work is compared with the machine learning algorithms like Gated Recurrent Unit (GRU), Long Short-Term Memory Neural Network (LSTM), and Bidirectional Long Short-Term Memory Neural Network (Bi-LSTM). Various existing approaches for the forecasting of wind speed like physical and statistical models, along with artificial intelligence models is used by numerous researchers. The dataset has been received from various authenticated sources like Windmills in Jaipur, globalwindatlas.info ...

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Publisher Resources

ISBN: 9781119761693Purchase Link