A novel hybrid algorithm based on Empirical Fourier decomposition and deep learning for wind speed forecasting

Kumar, Bhupendra; Yadav, Neha; Sunil,

doi:10.1016/j.enconman.2023.117891

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…In this respect, many non-machine learning (nML) as well as machine learning (ML) techniques have been developed to predict wind power and speed using weather data and historical records such as Jamii et al (2022) , Mujeeb et al (2019) and Magadum et al (2023) . ML techniques such as neural networks and LSTM can estimate wind speed better than archaic numerical weather prediction methods ( Zhang et al, 2022 ; Peng et al, 2021 ; Peng et al, 2020 ; Kumar & Yadav, 2024 ; Wang et al, 2024 ).…”

Section: Introductionmentioning

confidence: 99%

Short-term wind power forecasting through stacked and bi directional LSTM techniques

Ali Khan,

Khan,

Shah

et al. 2024

PeerJ Computer Science

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Background Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem. This, in turn, improves training performance. Methods The RNN model is developed based on stack LSTM and bidirectional LSTM. The parameters like mean absolute error (MAE), standard deviation error (SDE), and root mean squared error (RMSE) are utilized as performance measures for comparison with recent state-of-the-art techniques. Results Results showed that the proposed technique outperformed the existing techniques in terms of RMSE and MAE against all the used wind farm datasets. Whereas, a reduction in SDE is observed for larger wind farm datasets. The proposed RNN approach performed better than the existing models despite fewer parameters. In addition, the approach requires minimum processing power to achieve compatible results.

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