Online error correction method of PMU data based on LSTM model and Kalman filter

Zhou, Wenyue; Liu, Mingzhong; Zheng, Yuchuan; Dai, Yuhan

doi:10.1109/appeec48164.2020.9220710

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…In order to reduce the impact of overfitting on model prediction accuracy, Dropout regularization mechanism is added to the LSTM based on related research results [58][59][60][61][62]. The dropping rate, gradient threshold and initial learning rate are set to 0.2, 1 and 0.005, respectively.…”

Section: Hidden Layer Nodesmentioning

confidence: 99%

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

Zhang¹,

Li²,

Zhang³

et al. 2022

Journal of Renewable Materials

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Cemented paste backfill (CPB) is a sustainable mining technology that is widely used in mines and helps to improve the mine environment. To investigate the relationship between aggregate grading and different affecting factors and the uniaxial compressive strength (UCS) of the cemented paste backfill (CPB), Talbol gradation theory and neural networks is used to evaluate aggregate gradation to determine the optimum aggregate ratio. The mixed aggregate ratio with the least amount of cement (waste stone content river sand content = 7:3) is obtained by using Talbol grading theory and pile compactness function and combined with experiments. In addition, the response surface method is used to design strength-specific ratio experiments. The UCS prediction model which uses the LSTM and considers the aggregates gradation have high accuracy. The root mean square error (RMSE) of the prediction results is 0.0914, the coefficient of determination (R 2 ) is 0.9973 and the variance account for (VAF) is 99.73. Compared with back propagation neural network (BP-ANN), extreme learning machine (ELM) and radial basis function neural network (RBF-ANN), LSTM can effectively characterize the nonlinear relationship between UCS and individual affecting factors and predict UCS with high accuracy. The sensitivity analysis of different affecting factors on UCS shows that all 4 factors have significant effect on UCS and sensitivity is in the following ranking: cement content (0.9264) > slurry concentration (0.9179) > aggregate gradation (waste rock content) (0.9031) > curing time (0.9031).

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Section: Hidden Layer Nodesmentioning

confidence: 99%

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

Zhang¹,

Li²,

Zhang³

et al. 2022

Journal of Renewable Materials

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“…Two methods have been explored in the literature for error correction of numerical models (Bocquet et al, 2021; Chen et al, 2022; Zhou et al, 2020): (a) offline error correction and (b) online error correction. In the case of offline error correction, the correction can only begin once a large dataset has been accumulated.…”

Section: Introductionmentioning

confidence: 99%

A machine‐learning and data assimilation forecasting framework for surface waves

Pokhrel,

Abdelguerfi,

Ioup

2024

Quart J Royal Meteoro Soc

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In this paper, we combine Deep symbolic regression (DSR) and Ensemble Optimal Interpolation‐based Data Assimilation (DA) method to correct the error in the forecasts from the numerical model, WaveWatch III. In our experiments, the DA and DSR training is performed on the hindcasts and then the model is integrated forward in time with both the numerical model and the symbolic expressions generated from the DSR procedure to generate the forecasts. The DSR method is utilized in this paper to generate the symbolic equations that correct the model error in the WaveWatch III/ DA system. The proposed algorithm takes the zonal (u) and meridional (v) wind components from Global Forecast System (GFS) forecasts, wave heights from WaveWatch III, and geographical coordinates (latitude and longitude) to model physical relationships not included in the original numerical model. The DA is performed using JASON‐2 and SARAL altimeter measurements, and the independent testing uses the in situ buoys The RMSD of the proposed method is better than the numerical model with/without DA for up to 42 hours with only 12 days of assimilation spin‐up cycle. The symbolic equation generated from the proposed framework can be used to correct the predictions from WaveWatch III for weather prediction.This article is protected by copyright. All rights reserved.

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Campus Proxy Application Based on Cloud Computing, Social LSTM Technology and Extended Kalman Filter

2023

2023 3rd International Conference on Electronic Information Engineering and Computer (EIECT)

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Online error correction method of PMU data based on LSTM model and Kalman filter

Cited by 3 publications

References 10 publications

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

A machine‐learning and data assimilation forecasting framework for surface waves

Campus Proxy Application Based on Cloud Computing, Social LSTM Technology and Extended Kalman Filter

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