VMD-WSLSTM Load Prediction Model Based on Shapley Values

Shao, Bilin; Yan, Yichuan; Zeng, Huibin

doi:10.3390/en15020487

Cited by 3 publications

(1 citation statement)

References 32 publications

(37 reference statements)

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“…These gates enable the selection, forgetting, and outputting of information, respectively. LSTM addresses the issues of gradient vanishing and gradient explosion in RNN, allowing for more effective processing of time series data [10][11][12].…”

Section: Lstmmentioning

confidence: 99%

Prediction Method for Aging Life of Rubber O-Rings Based on GRU-LSTM Neural Network

Zheng,

Liu,

et al. 2023

Preprint

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This paper proposes a prediction model for rubber aging life based on the GRU-LSTM neural network to address the issue of sealing failure caused by material aging of rubber O-rings in the petrochemical industry. The model aims to overcome the limits of existing methods, which have low prediction accuracy and poor stability. In this study, we conducted rubber aging experiments to get data on rubber compression set at different temperatures. After preprocessing the data, we experimentally verified and compared the GRU-LSTM model with the two-layer LSTM model, two-layer GRU model, and BP neural network. Finally, we carried out an example analysis and application of high-temperature flange sealing in a chemical equipment. The results revealed significant differences between the two types of rubber, NBR-26 and HNBR. When using the aging data at 60, 80, and 100°C to predict the aging data at 70 and 90°C, the R2 values for NBR-26 and HNBR were 0.9968 and 0.9945, respectively. These findings highlight the superior predictive performance of the proposed model. These results show significantly better prediction performance compared to the other three models. Further, the accuracy of the prediction results increased with the number of temperature levels and samples. We recommend using at least three temperatures for prediction, and a larger sample size is preferable. The example analysis showed that this prediction model can help decide the replacement cycle of high-temperature flange rubber O-rings at different storage temperatures, reducing maintenance costs.

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Section: Lstmmentioning

confidence: 99%

Prediction Method for Aging Life of Rubber O-Rings Based on GRU-LSTM Neural Network

Zheng,

Liu,

et al. 2023

Preprint

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Microgrid Distributed Power Forecast Taking Multivariable Correlation and Weather Conditions into Account

Na,

Kaihan

2024

2024 IEEE 2nd International Conference on Power Science and Technology (ICPST)

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Point-Interval Forecasting for Electricity Load Based on Regular Fluctuation Component Extraction

2023

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The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting and propose a point-interval forecasting model for electricity load based on regular fluctuation component extraction. Firstly, the variational modal decomposition is combined with the sample entropy to decompose the original load series into a strong regular fluctuation component and a weak regular fluctuation component. Then, the gate recurrent unit neural network is used for point forecasting of the strong regular fluctuation component, and the support vector quantile regression model is used for interval forecasting of the weak regular fluctuation component, and the results are accumulated to obtain the final forecasting intervals. Finally, experiments were conducted using electricity load data from two regional electricity grids in Shaanxi Province, China. The results show that combining the idea of point interval, point forecasting, and interval forecasting for components with different fluctuation regularity can effectively reduce the forecasting interval width while having high accuracy. The proposed model has higher forecasting accuracy and smaller mean interval width at various confidence levels compared to the commonly used models.

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VMD-WSLSTM Load Prediction Model Based on Shapley Values

Cited by 3 publications

References 32 publications

Prediction Method for Aging Life of Rubber O-Rings Based on GRU-LSTM Neural Network

Prediction Method for Aging Life of Rubber O-Rings Based on GRU-LSTM Neural Network

Microgrid Distributed Power Forecast Taking Multivariable Correlation and Weather Conditions into Account

Point-Interval Forecasting for Electricity Load Based on Regular Fluctuation Component Extraction

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