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

Kosana, Vishalteja; Teeparthi, Kiran; Madasthu, Santhosh

doi:10.1007/s00521-022-07125-4

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…In [71], Gauss-Bernoulli RBM and Bernoulli-Bernoulli RBM were combined in a deep belief network (DBN), using GBRBM as the initial RBM to transform the continuity features of the original wind speed data into binomial distribution features. In [72], a one-dimensional CNN was employed as an encoder to extract important features and form latent representations. The decoding network, bidirectional LSTM (BiLSTM) [73], predicted wind speed by interpreting the encoded features.…”

Section: Neural Network-based Methodsmentioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

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Section: Neural Network-based Methodsmentioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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“…Recently, researchers have studied a number of deep learning networks. A CNN-LSTM combination model has been applied to predict wind speed, PM2.5, and ozone concentration [16][17][18][19] . Therefore, it is evident that deep learning network models have been applied for simulations and predictions in the atmospheric environment and meteorological fields.…”

Section: Introductionmentioning

confidence: 99%

Attention-based CNN–LSTM deep learning hybrid model for the prediction of atmospheric ozone concentration

JIANG,

HUA,

WANG

et al. 2024

Preprint

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Considering that ozone is essential to understanding air quality and climate change, this study introduces a deep learning method for predicting atmospheric ozone concentrations. The method combines an attention mechanism with a convolutional neural network (CNN) and long short-term memory (LSTM) to address the nonlinear nature of multivariate time-series data. It employs CNN and LSTM to extract features from short series, enhanced by the attention mechanism for improved short-term prediction accuracy. The model uses eight meteorological and environmental parameters from 16,806 records (2018–2019) as input, selected through principal component analysis (PCA). It features a hybrid attention-CNN-LSTM model with specific settings: a time step of 5, a batch size of 25, 15 units in the LSTM layer, the Relu activation function, 25 epoch iterations, and an overfitting avoidance strategy at 0.15. Experimental results demonstrate that this hybrid model outperforms independent models and the CNN-LSTM model, especially in forward prediction with a multi-hour time lag. The model exhibits a high prediction determination coefficient (R2 = 0.971) and a root mean square error of 3.59 for a 1-hour time lag. It also shows consistent accuracy across different seasons, highlighting its robustness and superior time-series prediction capabilities for ozone concentration.

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