Short-term wind power forecasting based on Attention Mechanism and Deep Learning

Xiong, Bangru; Lou, Lu; Meng, Xinyu; Wang, Xin; Ma, Hui; Wang, Zhengxia

doi:10.1016/j.epsr.2022.107776

Cited by 96 publications

(16 citation statements)

References 28 publications

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“…Since the accuracy of multi-step iterative prediction decreases with increasing step length [49,50], it is particularly important to obtain accurate first-step prediction values. To verify the prediction accuracy of the combined prediction model proposed in this paper, KELM, IBAS-KELM, FPA-KELM (KELM improved by the flower pollination algorithm), PSO-KELM (KELM improved by the particle swarm algorithm), CEEMDAN-SE-IBAS-KELM, and the CEEMDAN-SE-KELM combined with error correction proposed in this paper were constructed -IBAS-KELM combined with error correction.…”

Section: Wind Power Prediction Experimentsmentioning

confidence: 99%

Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction

Wang

Shan

2022

Energies

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The instability of wind power poses a great threat to the security of the power system, and accurate wind power prediction is beneficial to the large-scale entry of wind power into the grid. To improve the accuracy of wind power prediction, a short-term multi-step wind power prediction model with error correction is proposed, which includes complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), sample entropy (SE), improved beetle antennae search (IBAS) and kernel extreme learning machine (KELM). First, CEEMDAN decomposes the original wind power sequences into a set of stationary sequence components. Then, a set of new sequence components is reconstructed according to the SE value of each sequence component to reduce the workload of subsequent prediction. The new sequence components are respectively sent to the IBAS-KELM model for prediction, and the wind power prediction value and error prediction value of each component are obtained, and the predicted values of each component are obtained by adding the two. Finally, the predicted values of each component are added to obtain the final predicted value. The prediction results of the actual wind farm data show that the model has outstanding advantages in high-precision wind power prediction, and the error evaluation indexes of the combined model constructed in this paper are at least 34.29% lower in MAE, 34.53% lower in RMSE, and 36.36% lower in MAPE compared with other models. prediction decreased by 30.43%, RMSE decreased by 29.67%, and MAPE decreased by 28.57%, and the error-corrected three-step prediction decreased by 55.60%, RMSE decreased by 50.00%, and MAPE decreased by 54.17% compared with the uncorrected three-step prediction, and the method significantly improved the prediction accuracy.

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Section: Wind Power Prediction Experimentsmentioning

confidence: 99%

Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction

Wang

Shan

2022

Energies

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“…Chen et al [ 25 ] designed a weighted combination prediction composed of six long short-term memory networks (LSTM), and its prediction effect is better than that of a single prediction model. Xiong et al [ 26 ] proposed a multi-scale hybrid prediction model that combines attention mechanism, CNN, and LSTM to adequately capture the high-dimensional features in wind farm data. Zheng et al [ 27 ] established a hybrid model combining bidirectional long-short-term memory (Bi-LSTM) and CNN, which adopted a unique feature extraction method of space and then time.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer

Tian

Wang

Zhou

et al. 2023

Entropy

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Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction.

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“…Artificial intelligence technology has outstanding advantages in dealing with non-linear problems, and many researchers have applied artificial intelligence methods to the field of wind power forecasting (Ogliari et al, 2021;Wang et al, 2021;Chen et al, 2022a). Artificial intelligence methods mainly include BP neural network (BPNN) (Zhu et al, 2022), support vector machine (SVM) (Li et al, 2020) , extreme learning machine (ELM) (Peng et al, 2017), generalized regression neural network (GRNN) (Ding et al, 2021) and Long-term and Short-term Memory network (LSTM) (Xiong et al, 2022) etc. Ren et al (Ren et al, 2014) proposed an IS-PSO-BP wind speed forecasting model, which achieved good forecasting performance.…”

Section: Introductionmentioning

confidence: 99%

A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting

Zheng²,

Lu³

2023

Front. Energy Res.

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Short-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing and the limitations of a single forecasting model, resulting in low forecasting accuracy. Aiming at the shortcomings of the existing models, a combined forecasting model based on secondary decomposition technique and grey wolf optimizer (GWO) is proposed. In the process of forecasting, firstly, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and wavelet transform (WT) are used to preprocess the wind power data. Then, least squares support vector machine (LSSVM), extreme learning machine (ELM) and back propagation neural network (BPNN) are established to forecast the decomposed components respectively. In order to improve the forecasting performance, the parameters in LSSVM, ELM, and BPNN are tuned by GWO. Finally, the GWO is used to determine the weight coefficient of each single forecasting model, and the weighted combination is used to obtain the final forecasting result. The simulation results show that the forecasting model has better forecasting performance than other forecasting models.

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Short-term wind power forecasting based on Attention Mechanism and Deep Learning

Cited by 96 publications

References 28 publications

Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction

Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer

A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting

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