A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error

Duan, Jikai; Chang, Mingheng; Chen, Xiangyue; Wang, Wenpeng; Zuo, Hongchao; Bai, Yulong; Chen, Bolong

doi:10.1016/j.renene.2022.09.114

Cited by 67 publications

(13 citation statements)

References 51 publications

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“…Movement phase: according to the DLH strategy, the updated position of wolf U v (h) is calculated by the equation (18). Simultaneously generates another candidate location U I−GWO (h + 1) for wolf U v (h).…”

Section: ) Kurtosis Difference Coefficient Index (Kdci)mentioning

confidence: 99%

“…When the neighborhood of U v (h) is constructed, multineighborhood location learning can be performed by equation (18), where the tth dimension U I−DLH,t (h + 1) is obtained by the computing of a randomly selected domain M v (h) from U m, t (h), and a randomly selected U r, t (h) from Pop.…”

Section: ) Kurtosis Difference Coefficient Index (Kdci)mentioning

confidence: 99%

“…Chen employed the long short-term memory model (LSTM) to accurately implement condition monitoring and health management of gearboxes [17]. Duan proposed to combine convolutional neural network (CNN) with recurrent neural network (RNN), and meanwhile take into account the model error to improve the prediction of wind speed, which results showcased a significant improvement in prediction accuracy compared to individual models [18]. Nevertheless, conventional LSTM and CNN models exhibit limitations.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of aero-engine feature-hierarchy fusion degradation trend based on parameter-adaptive VMD method and improved transformer model

Lu,

Jiang,

et al. 2024

Meas. Sci. Technol.

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The accumulation of operational time in aero-engines leads to irreversible mechanical wear and tear, necessitating accurate measurement of the health evolution trend for effective predictive maintenance, thus reducing the risk of accidents and ensuring personnel safety. In this paper, a parameter-adaptive variational mode decomposition (VMD) method and improved transformer model are proposed to forecast the degradation trend of aero-engine feature hierarchy fusion. Firstly, in order to quantitatively evaluate the engine health evolution process, the health state aggregate indicator (HSAI) is innovatively constructed by employing the deep blend auto-encoder and self-organizing map network, which facilitate the feature-hierarchy fusion of multi-source sensory data. Secondly, for the significant characteristics with nonlinearity and stochastic fluctuation of the HSAI sequence, the multiscale frequency features are extracted by the parameter-adaptive VMD method with the improved gray wolf optimizer, which analyzes the inherent degradation law. Finally, considering the problem of parameter sharing in the transformer model, a simplified mixture of experts routing algorithm is introduced to implement the switch transformer model to further measure the future aero-engine health trends. Extensive experiments on the multi-source dataset of aero-engine confirm that the proposed method accomplishes the more superior performance for health evolution measurement compared with other available methods.

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Section: ) Kurtosis Difference Coefficient Index (Kdci)mentioning

confidence: 99%

Section: ) Kurtosis Difference Coefficient Index (Kdci)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measurement of aero-engine feature-hierarchy fusion degradation trend based on parameter-adaptive VMD method and improved transformer model

Lu,

Jiang,

et al. 2024

Meas. Sci. Technol.

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“…Convolutional neural networks can be divided into 1D CNNs [38], 2D CNNs [39], and 3D CNNs. The CNN performs feature extraction on input data through operations such as convolution, activation functions, and pooling [40,41]. Compared to the 1D CNN and 2D CNN, the convolution kernel of the 3D CNN not only slides in the spatial dimension but also in the temporal dimension (Figure 1).…”

Section: Three-dimensional Convolutional Neural Networkmentioning

confidence: 99%

A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

Huang,

Zhang,

Liu

et al. 2023

Sustainability

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Enhancing the accuracy of short-term wind power forecasting can be effectively achieved by considering the spatial–temporal correlation among neighboring wind turbines. In this study, we propose a short-term wind power forecasting model based on 3D CNN-GRU. First, the wind power data and meteorological data of 24 surrounding turbines around the target turbine are reconstructed into a three-dimensional matrix and inputted into the 3D CNN and GRU encoders to extract their spatial–temporal features. Then, the power predictions for different forecasting horizons are outputted through the GRU decoder and fully connected layers. Finally, experimental results on the SDWPT datasets show that our proposed model significantly improves the prediction accuracy compared to BPNN, GRU, and 1D CNN-GRU models. The results show that the 3D CNN-GRU model performs optimally. For a forecasting horizon of 10 min, the average reductions in RMSE and MAE on the validation set are about 10% and 11%, respectively, with an average improvement of about 1% in R. For a forecasting horizon of 120 min, the average reductions in RMSE and MAE on the validation set are about 6% and 8%, respectively, with an average improvement of about 14% in R.

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“…The methods and comparison methods used in this paper. Such as generalized regression neural network (GRNN) model, short‐term memory neural network LSTM, 23 support vector regression SVR, 24 cyclic neural network RNN, 25 RNN‐CNN neural network fusion algorithm, 26 generative adversarial networks (GAN) 27 Neural Architecture Search (NAS) 28 and other modern intelligent algorithms, are some of the most commonly used forecasting methods. These methods are based on more perfect theoretical knowledge and more developed science and technology innovation.…”

Section: Introductionmentioning

confidence: 99%

Short‐term load forecasting based on a generalized regression neural network optimized by an improved sparrow search algorithm using the empirical wavelet decomposition method

Fan¹,

Li²,

Zhang³

et al. 2023

Energy Science & Engineering

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With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy.

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A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error

Cited by 67 publications

References 51 publications

Measurement of aero-engine feature-hierarchy fusion degradation trend based on parameter-adaptive VMD method and improved transformer model

Measurement of aero-engine feature-hierarchy fusion degradation trend based on parameter-adaptive VMD method and improved transformer model

A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

Short‐term load forecasting based on a generalized regression neural network optimized by an improved sparrow search algorithm using the empirical wavelet decomposition method

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