Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review
| Autor: | Senthil Kumar Paramasivan |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Revue d'Intelligence Artificielle. 35:1-10 |
| ISSN: | 1958-5748 0992-499X |
| DOI: | 10.18280/ria.350101 |
| Popis: | In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques. |
| Databáze: | OpenAIRE |
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