Multistep Wind Speed Forecasting Based on a Hybrid Model of VMD and Nonlinear Autoregressive Neural Network

Yan, Zheng; Dong, Bo; Liu, Yuhan; Tong, Xiaolei; Wang, Lei

doi:10.1155/2021/6644668

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…ANNs (Zheng et al, 2021) This developed model improved the accuracy by 61.90% in 24 hours on wind speed forecasting, and the predicted time horizon was improved by 6.8 hours.…”

Section: Methodsmentioning

confidence: 96%

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

Chen,

Wang,

Zhang

et al. 2024

Journal of Forecasting

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Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

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“…ANNs (Zheng et al, 2021) This developed model improved the accuracy by 61.90% in 24 hours on wind speed forecasting, and the predicted time horizon was improved by 6.8 hours.…”

Section: Methodsmentioning

confidence: 96%

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

Chen,

Wang,

Zhang

et al. 2024

Journal of Forecasting

View full text Add to dashboard Cite

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“…The main purpose of data preprocessing is to decompose the wind speed data to reduce the effect of nonlinear effects. Zheng et al (2021) achieved the decomposition of wind speed signal by the VMD method, which further extracted the time-frequency information to achieve wind speed prediction. Wang et al (2021) proposed a two-stage data preprocessing method by VMD and Sim Geometric Mode Decomposition (SGMD), and the proposed method is experimentally demonstrated to be suitable for nonlinear wind speed analysis.…”

Section: Related Workmentioning

confidence: 99%

A wind speed interval prediction method for reducing noise uncertainty

Li,

Liu,

Han

2024

Wind Engineering

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Due to the noise uncertainty, the conventional point prediction model is difficult to describe the actual characteristics of wind speed and lacks a description of the wind speed fluctuation range. In this paper, the kernel density estimation according to its error value is given, and then its fluctuation range is found to combine the prediction results of the test set to get its prediction range. Firstly, the singular spectrum analysis (SSA) is introduced to conduct the noise reduction, and variational modal decomposition (VMD) is performed to handle the sequences, then an improved slime mold algorithm (SMA) is proposed to optimize the VMD, and the stochastic configuration networks (SCNs) is applied to perform the prediction. Finally, the interval prediction results are calculated by fusing the point prediction error and kernel density estimation. The experimental results demonstrate that the proposed method can effectively reduce the noise interference in the wind speed prediction.

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“…Liu et al [31] used the wavelet decomposition method to process the data and combined it with a hybrid optimization framework for multi-step wind speed prediction. Zheng et al [32] combined VMD with neural networks to realize the multi-step prediction of wind speed data. Wang et al [33] considered the similarity characteristics of data between multiple sites to improve the multi-step wind speed prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the determination of the decomposition dimension, i.e., the number of decompositions, of the raw wind speed data, the center frequency can be used for discrimination. After the decomposition of the original signal data, the center frequency of adjacent decomposition signals is compared, and if the relative value of the center frequency exceeds 90%, it is considered that the decomposition is excessive, and the maximum number of undecomposed excessive modes is selected as the target number for VMD [32]. In this paper, in order to simplify the data preprocessing steps, the VMD dimension of wind speed data in existing studies is used as the decomposition parameter.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

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Multistep Wind Speed Forecasting Based on a Hybrid Model of VMD and Nonlinear Autoregressive Neural Network

Cited by 6 publications

References 21 publications

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

A wind speed interval prediction method for reducing noise uncertainty

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

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