Short-Term Wind Power Forecasting Using R-LSTM

Shobanadevi Ayyavu, G. Maragatham, M. R. Prabu, K. Boopathi

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


Long-Short Term Memory (LSTM), Wind Energy, Short-Term Wind Power Forecasting, Deep Learning.

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DOI (PDF): https://doi.org/10.20508/ijrer.v11i1.11807.g8144

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