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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

7Deep Feature Selection for Wind Forecasting-I

C. Ramakrishnan1*, S. Sridhar2, Kusumika Krori Dutta2, R. Karthick1 and C. Janamejaya2

1Dept. of EEE, SNS College of Technology, Coimbatore, India

2Dept. of EEE, M S Ramaiah Institute of Technology, Bangalore, India

Abstract

The existence of abundant renewable energy and fast growing technologies in wind energy extraction creates extensive attention on the wind forecasting. The forecastings are generally very short-term forecasting (few seconds to 30 minutes ahead), short-term forecasting (30 minutes to several hour ahead), medium-term forecasting (several hours to 1 week ahead), and long-term forecasting (from 1 week to 1 year or more). The forecasting involves extraction of single or multiple features from the time series data for more accurate prediction. The different wind speed and power forecasting model include physical model, statistical model, computational model, and hybrid model. Pre-processing the raw data, feature extraction, and prediction are the steps involved in forecasting of wind speed and wind energy. The accuracy in prediction cannot be as expected due to complex computations when mathematical models have been used though it is simple. To improve the performance of prediction and reduce the computational time, computational intelligence (CI) methods like artificial neural networks, evolutionary computation, fuzzy logic, and probabilistic methods are employed for analyzing, decision-making, and optimizing the ...

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Publisher Resources

ISBN: 9781119761693Purchase Link