An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms

Yu, Enbo; Xu, Guoji; Yan, Huang; Li, Yongle

doi:10.1016/j.energy.2022.124569

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…Many ML algorithms such as autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Bayesian approach, etc., have been investigated within the wind engineering field for forecasting wind speed estimation [55]. In [56], the authors employ a convolutional neural network (CNN) and long short-term memory (LSTM) to predict and increase the accuracy of wind based on a measured 10 min average wind speed data. The paper claims that the models have a strong learning ability for new data with increased accuracy.…”

Section: Methodsmentioning

confidence: 99%

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Nithya,

Quaranta,

Muscarello

et al. 2024

Drones

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Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.

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Section: Methodsmentioning

confidence: 99%

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Nithya,

Quaranta,

Muscarello

et al. 2024

Drones

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“…As short-term power load forecasting relies more on historical information, ensuring the complete transmission of historical feature information is a pressing issue to be addressed in TCN-GRU networks. Therefore, in 2022, Yu et al introduced an attention mechanism by adding a weight matrix that receives gradient backpropagation in the convolutional layer and trained it using a convolutional neural network in wind speed forecasting [30]. This approach allowed the model to focus more on key components.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model

Hong

Wang

et al. 2023

Sustainability

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Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models.

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“…However, unstable and intermittent wind speed causes fluctuations in the generation of wind power and affects the utilization of wind energy, seriously hindering the advance of wind power generation. Therefore, accurate wind speed prediction is crucial to improving the efficiency and stability of wind power generation [3]. The change in the wind speed needs to be predicted so that wind turbine can be regulated prior to such change to maximize the utilization of wind energy while reducing the fluctuation and instability caused by changing wind speed.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer

Xinxin,

Xiaopan,

Xueyi

et al. 2023

PLoS ONE

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Wind energy, as a kind of environmentally friendly renewable energy, has attracted a lot of attention in recent decades. However, the security and stability of the power system is potentially affected by large-scale wind power grid due to the randomness and intermittence of wind speed. Therefore, accurate wind speed prediction is conductive to power system operation. A hybrid wind speed prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Multiscale Fuzzy Entropy (MFE), Long short-term memory (LSTM) and INFORMER is proposed in this paper. Firstly, the wind speed data are decomposed into multiple intrinsic mode functions (IMFs) by ICEEMDAN. Then, the MFE values of each mode are calculated, and the modes with similar MFE values are aggregated to obtain new subsequences. Finally, each subsequence is predicted by informer and LSTM, each sequence selects the one with better performance than the two predictors, and the prediction results of each subsequence are superimposed to obtain the final prediction results. The proposed hybrid model is also compared with other seven related models based on four evaluation metrics under different prediction periods to verify its validity and applicability. The experimental results indicate that the proposed hybrid model based on ICEEMDAN, MFE, LSTM and INFORMER exhibits higher accuracy and greater applicability.

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An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms

Cited by 14 publications

References 23 publications

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model

Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer

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