Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

Zhang, Yilin

doi:10.1155/2022/2871889

Cited by 7 publications

(12 citation statements)

References 34 publications

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“…Then, the LSTM was used to train these features to make prediction. Besides, this deep learning method is also applicable to load forecasting 17–20 . That is to say, the application of the hybrid CNN‐LSTM neural network is not limited by the data type.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, this deep learning method is also applicable to load forecasting. [17][18][19][20] That is to say, the application of the hybrid CNN-LSTM neural network is not limited by the data type. In this study, the loading sequence refers to a set of loading points with time-series features that characterize the history of multiaxial loading.…”

Section: Introductionmentioning

confidence: 99%

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Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Heng

Gao

et al. 2023

Fatigue Fract Eng Mat Struct

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A new algorithm optimization‐based hybrid neural network model is proposed in the present study for the multiaxial fatigue life prediction of various metallic materials. Firstly, a convolutional neural network (CNN) is applied to extract the in‐depth features from the loading sequence composed of the critical fatigue loading conditions. Meanwhile, the multiaxial historical loading information with time‐series features is retained. Then, a long short‐term memory (LSTM) network is adopted to capture the time‐series features and in‐depth features of the CNN output. Finally, a full connection layer is used to achieve dimensional transformation, which makes the fatigue life predictable. Herein, the hyperparameters of the LSTM network are automatically determined using the slime mold algorithm (SMA). The test results demonstrate that the proposed model has pleasant prediction performance and extrapolation capability, and it is suitable for the life prediction of various metallic materials under uniaxial, proportional multiaxial, nonproportional multiaxial loading conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Heng

Gao

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…Heng et al 27 proposed a multiaxial fatigue life prediction model of various metal materials model based on a CNN–LSTM hybrid neural network. In addition, the CNN hybrid neural network can also be used for lithium battery remaining life and load prediction 28–31 . The entire process makes full use of the feature extraction capabilities of CNN, combined with other types of neural networks, such as long short‐term memory neural networks (LSTM) and gated recurrent units (GRU), to show good prediction capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the CNN hybrid neural network can also be used for lithium battery remaining life and load prediction. [28][29][30][31] The entire process makes full use of the feature extraction capabilities of CNN, combined with other types of neural networks, such as long short-term memory neural networks (LSTM) and gated recurrent units (GRU), to show good prediction capabilities. These studies indicate that CNN hybrid neural networks have been widely applied.…”

mentioning

confidence: 99%

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

Ma,

Tian,

Sun

et al. 2024

Fatigue Fract Eng Mat Struct

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This study proposes a fatigue life prediction method for composite bolted joints, which combines algorithm optimization‐based hybrid neural networks with finite element modeling. First, based on the Hashin failure criterion of physical mechanism, a finite element model for fatigue life prediction of composite bolted joints is established, and the simulation calculations have been conducted using various initial conditions. Then, by integrating the simulation and experiment data, we have established a fatigue life database that serves machine learning training and prediction. Finally, the data undergo a comprehensive process of deep feature extraction through the utilization of a convolutional neural network (CNN). The resulting deep features are utilized as inputs for training the backpropagation neural network (BPNN) to predict fatigue life. The results indicate this synergistic combination of CNN and BPNN results in a substantial improvement in prediction accuracy and has remarkable superiority in predicting the fatigue life of composite bolted joints.

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“…Although traditional statistical methods have high computational efficiency, they require high stationarity of time series data [3]. With the increase in computing power, machine learning methods have received more and more attention in the task of power forecasting due to their good ability to deal with complex data.…”

Section: Introductionmentioning

confidence: 99%

A New Energy Power Forecasting Method using Neural Networks with Autocorrelated Errors

Qu¹,

Lu²,

Ding³

et al. 2022

J. Phys.: Conf. Ser.

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With the transformation and upgrading of the energy structure, the installed capacity and power generation of new energy have continued to grow in recent years. Due to the randomness of new energy, the impact on the grid will increase with its large-scale grid connection. Therefore, in order to ensure the stability of the power grid, it is necessary to make a reasonable forecast of the new energy power. So far, there have been many authors using deep methods to perform new energy power forecasting. However, these methods usually assume the errors of the model across different time steps are independent, which is inaccurate because of the temporality of the series. In this paper, a method that combines neural networks with autocorrelation errors is proposed for new energy power forecasting. Through the ablation experiments in difference datasets, we verify the effectiveness of the methods.

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Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

Cited by 7 publications

References 34 publications

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

A New Energy Power Forecasting Method using Neural Networks with Autocorrelated Errors

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