A Comprehensive Review on Deep Learning Algorithms for Wind Power Prediction

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Published Nov 18, 2022
https://doi.org/10.47164/ijngc.v13i4.631
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Volume 13, Issue 4, November 2022

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Geetika Sharma
Research Scholar
Madan Lal
Kanwal Preet Singh Attwal

Abstract

In recent years, various energy crisis and environmental considerations have prompted the use of renewable energy resources. Renewable energy resources like solar, wind, hydro, biomass, etc. have been a continuous source of clean energy. Wind energy is one of the renewable energy resources that has been widely used all over the world. The wind power is mainly dependent on wind speed which is a random variable and its unpredictable behavior creates various challenges for wind farm operators like energy dispatching and system scheduling. Hence, predicting wind power energy becomes crucial. This        has led to the development of various forecasting models in the recent decades. The most commonly used deep learning algorithms for wind power prediction are- RNN (Recurrent Neural Network), LSTM (Long Short- Term Memory) and CNN (Convolutional Neural Network). This paper presents the working of these algorithms and provides a timeline review of the research papers that used these algorithms for wind power prediction.

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How to Cite
Geetika Sharma, Madan Lal, & Kanwal Preet Singh Attwal. (2022). A Comprehensive Review on Deep Learning Algorithms for Wind Power Prediction. International Journal of Next-Generation Computing, 13(4). https://doi.org/10.47164/ijngc.v13i4.631
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