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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Bibliographic Details
Published in:Neural computing & applications Vol. 34; no. 15; pp. 12653 - 12662
Main Authors: Kosana, Vishalteja, Teeparthi, Kiran, Madasthu, Santhosh
Format: Journal Article
Language:English
Published: London Springer London 01.08.2022
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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 uncertainty modelling. Encoder and decoder are the two parts of the proposed hybrid model. In this study, a one-dimensional convolutional neural network (1D-CNN) is used as the encoder, and a bidirectional long short term memory network (Bi-LSTM) is used as the decoder. Encoder extracts the important characteristics and forms a latent representation. Then, wind speed is predicted by the decoder network by interpreting the characteristics of the encoded representation. The hybrid approach is validated using several regular and widely used benchmark forecasting models to assess and examine its prediction performance. The prediction results using the real-time dataset obtained from a wind measuring station in Idalia, Colorado are used for performance evaluation. The performance validation analysis showed that the proposed hybrid approach has an improvement of 42% over the reference approaches.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07125-4