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

• Published in: PeerJ. Computer science Vol. 10; p. e1949
• Main Authors: Ali Khan, Mehmood, Khan, Iftikhar Ahmed, Shah, Sajid, EL-Affendi, Mohammed, Jadoon, Waqas
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29.03.2024; PeerJ Inc
• Subjects: Artificial Intelligence; Bidirectional LSTM; Data Mining and Machine Learning; Deep neural network; Green technology; Long short-term memory; Methods; Neural Networks; Recurrent neural network; Scientific Computing and Simulation; Stacked LSTM; Wind power; Wind power forecasting; Long short-term memory; Deep neural network; Bidirectional LSTM; Stacked LSTM; Recurrent neural network; Wind power forecasting
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1949

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. 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 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.