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  Citation Number 1
 Views 18
Short-Term Wind Power Forecasting Using R-LSTM
2021
Journal:  
International Journal of Renewable Energy Research
Author:  
Abstract:

Renewable energies such as wind and solar begin receiving remarkable popularity in accordance with the energy demand, expeditious expansion of solar and wind energy generation involves acute forecasting of wind and solar power, so in past and recent years it has become an intensive research area. An accurate forecast of wind power to maintain an affordable, secure, and economical power supply is most significant. Numerous investigations and research have been performed in this area in recent years. This research article aims to develop a short-term wind power forecasting model to improve the accuracy of the prediction. Therefore, a novel approach based on LSTM (Long Short-Term Memory) is proposed to forecast from 1 to 6 hours ahead of wind power. A recursive strategy is used when predicting short-term wind power, unlike the conventional LSTM approach. The proposed model is implemented using historical generated wind power data for Gujarat state. A comparative analysis is performed between the proposed and existing approach presented in the literature, from the analysis, it is noted that the proposed R-LSTM (Rolling-LSTM) model outperformed with minimal error and better accuracy. <

Keywords:

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International Journal of Renewable Energy Research

Field :   Eğitim Bilimleri; Fen Bilimleri ve Matematik; Sağlık Bilimleri; Sosyal, Beşeri ve İdari Bilimler

Journal Type :   Uluslararası

Metrics
Article : 1.313
Cite : 745
2023 Impact : 0.099
International Journal of Renewable Energy Research