Hybrid convolutional Bi-LSTM autoencoder framework for short-term wind speed prediction

Accurate wind speed prediction is essential for optimal operation and planning. The unstable and stochastic nature of the wind makes the task complicated and challenging. As a result, a hybrid approach is implemented to enhance prediction accuracy and to overcome the difficulties and challenges in u...

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Vydané v:	Neural computing & applications Ročník 34; číslo 15; s. 12653 - 12662
Hlavní autori:	Kosana, Vishalteja, Teeparthi, Kiran, Madasthu, Santhosh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.08.2022 Springer Nature B.V
Predmet:	Accuracy Alternative energy Artificial Intelligence Artificial neural networks Coders Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Electricity distribution Image Processing and Computer Vision Neural networks Original Article Performance evaluation Probability and Statistics in Computer Science Renewable resources Representations Wavelet transforms Weather forecasting Wind farms Wind power Wind speed Wind speed forecasting (WSF) Hybrid model Deep neural network models Convolutional neural network (CNN)
ISSN:	0941-0643, 1433-3058
On-line prístup:	Získať plný text
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