Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China

Yu, Chengqing; Yan, Guangxi; Yu, Chengming; Mi, Xiwei

doi:10.1016/j.asoc.2023.110864

Cited by 16 publications

(2 citation statements)

References 64 publications

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“…While combining various classical feature extraction methods helps in selecting better input features, it is ineffective in extracting deep and highly nonlinear features from complex wind data. Methods such as autoencoder (AE), variational AE (VAE), restricted Boltzmann machine (RBM), CNN [68], temporal convolutional network (TCN) [69], and attention mechanism [70] have been proven to be effective tools for nonlinear feature extraction and widely applied in the field of WSP and WPP. In [65], the VMD technique was employed to decompose the original wind speed sequence, obtaining relatively stable wind speed sequences.…”

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

View full text Add to dashboard Cite

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“…To further validate the proposed VMD-MSI-GTTS model in the offshore wind turbine power forecasting problem with LSTM [39], CNN-LSTM [40], LSTM-Attention [41], and Informer [42,43] models for comparison experiments. The results of the comparison experiments are shown in Table 6, and the visualization results are shown in Figure 15.…”

Section: Model Comparison Experimental Analysis and Validationmentioning

confidence: 99%

Multistep Forecasting Method for Offshore Wind Turbine Power Based on Multi-Timescale Input and Improved Transformer

Wan,

Gong,

Wei

et al. 2024

JMSE

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Wind energy is highly volatile, and large-scale wind power grid integration significantly impacts grid stability. Accurate forecasting of wind turbine power can improve wind power consumption and ensure the economy of the power grid. This paper proposes a multistep forecasting method for offshore wind turbine power based on a multi-timescale input and an improved transformer. First, the wind speed sequence is decomposed by the VMD method to extract adequate timing information and remove the noise, after which the decomposition signals are merged with the rest of the timing features, and the dataset is split according to different timescales. A GRU receives the short-timescale inputs, and the Improved Transformer captures the timing relationship of the long-timescale inputs. Finally, a CNN is used to extract the information of each time point at the output of each branch, and the fully connected layer outputs multistep forecasting results. Experiments were conducted on operation data from four wind turbines located within the offshore wind farm but not near the edge. The results show that the proposed method achieved average errors of 0.0522 in MAE, 0.0084 in MSE, and 0.0907 in RMSE on a four-step forecast. This outperformed comparison methods LSTM, CNN-LSTM, LSTM-Attention, and Informer. The proposed method demonstrates superior forecasting performance and accuracy for multistep offshore wind turbine power forecasting.

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