Offshore Wind Power Prediction Based on Variational Mode Decomposition and Long Short Term Memory Networks

Yang, Shuangmian; Zheng, Xuelian; Li, Meina; Wei, Yanbin

doi:10.1109/icpre52634.2021.9635584

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…K and α in the VMD decomposition parameters have a large impact on the decomposition effect. In power prediction applications, they are generally determined with empirical settings or the center frequency method [34]. However, due to the strong volatility of wind power series, the above two parameter setting methods are more subjective and random, and the decomposition is less effective.…”

Section: Decomposition Performance Evaluation Criteriamentioning

confidence: 99%

Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

Bai,

Jin,

et al. 2024

Processes

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Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode them. The gate recurrent unit (GRU) is decoded to achieve continuous power prediction. Based on the actual operating data of a wind farm, a case analysis is conducted. Experimental results show that SSA-VMD can effectively optimize the decomposition effect, and the subsequences obtained with its decomposition are highly accurate when applied to predictions. The Seq2Seq model has better multi-step prediction results than traditional prediction methods, and as the prediction step size increases, the advantages are more obvious.

show abstract

Section: Decomposition Performance Evaluation Criteriamentioning

confidence: 99%

Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

Bai,

Jin,

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…The effect of disjoint features on prediction can be avoided by predicting the partitioned components. Therefore, Yang et al (2021) decomposes the photovoltaic power output into components with different frequencies with Variational Mode Decomposition (VMD) and uses LSTM to predict them, then, integrates predicting results to get the final predicting result. In a similar manner, Wang et al (2020) uses a deep echo state network (DESN) to establish prediction models for each component obtained through VMD, and the predicting results are integrated to get the result.…”

Section: Introductionmentioning

confidence: 99%

Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM

Cheng,

Zheng,

Ruan

et al. 2023

Front. Energy Res.

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With the large-scale development of electric vehicles, the accuracy of electric vehicle charging load prediction is increasingly important for electric power system. Accurate EV charging load prediction is essential for the efficiency of electric system planning and economic operation of electric system. This paper proposes an electric vehicle charging load predicting method based on variational mode decomposition and Prophet-LSTM. Firstly, the variational mode decomposition algorithm is used to decompose the charging load into several intrinsic mode functions in order to explore the characteristics of EV charging load data. Secondly, in order to make full use of the advantages of various forecasting methods, the intrinsic mode functions are classified into low and high frequency sequences based on their over-zero rates. The high and low frequency sequences are reconstructed to obtain two frequency sequences. Then the LSTM neural network and Prophet model are used to predict the high and low frequency sequences, respectively. Finally, the prediction results obtained from the prediction of high frequency and low frequency sequences are combined to obtain the final prediction result. The assessment of the prediction results shows that the prediction accuracy of the prediction method proposed in this paper is improved compared to the traditional prediction methods, and the average absolute error is lower than that of ARIMA, LSTM and Prophet respectively by 7.57%, 8.73%, and 46.02%. The results show that the prediction method proposed in this paper has higher prediction accuracy than the traditional methods, and is effective in predicting EV charging load.

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“…Further, the modeling idea of deep learning is introduced to establish the neural network multistep prediction model [22], so as to improve the prediction performance of time series. Additionally, the prediction accuracy of this method is then compared with a traditional prediction method [23], VMD prediction method based on overlapping slicing, overall VMD prediction method [24], and overall VMD noise reduction prediction method [25].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method with Data Leakage Suppression for Time Series

et al. 2022

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In view of the characteristics of the collected time series, such as being high noise, non-stationary and nonlinear, most of the current methods are designed to smooth or denoise the whole time series at one time and then divide the training set and testing set, which will lead to using the information of the testing set in the training process, resulting in data leakage and other problems. In order to reduce the impact of noise on time series prediction and prevent data leakage, a prediction method with data leakage suppression for time series (DLS) is proposed. This prediction method carries out multiple variational mode decomposition on the time series by overlapping slicing and improves the noise reduction threshold function to perform noise reduction processing on the decomposed time series. Furthermore, the idea of deep learning is introduced to establish a neural network multi-step prediction model, so as to improve the performance of time series prediction. The different datasets are selected as experimental data, and the results show that the proposed method has a better prediction effect and lower prediction error, compared with the common multi-step prediction methods, which verifies the superiority of the prediction method.

show abstract

Offshore Wind Power Prediction Based on Variational Mode Decomposition and Long Short Term Memory Networks

Cited by 5 publications

References 13 publications

Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM

A Prediction Method with Data Leakage Suppression for Time Series

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