Situational awareness and deficiency warning system in a smart distribution network based on stacking ensemble learning

Ghaemi, Ali; Safari, Amin; Afsharirad, Hadi; Shayeghi, Hossein

doi:10.1016/j.asoc.2022.109427

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…The stacking ensemble learning model was introduced by wolpert [ 43 ]. Recently, it has been successfully applied in various applications [ 44 – 50 ]. It combines some models together in order to enhance accuracy, generalization ability, and robustness.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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The uniaxial compressive strength (UCS) and elasticity modulus (E) of intact rock are two fundamental requirements in engineering applications. These parameters can be measured either directly from the uniaxial compressive strength test or indirectly by using soft computing predictive models. In the present research, the UCS and E of intact carbonate rocks have been predicted by introducing two stacking ensemble learning models from non-destructive simple laboratory test results. For this purpose, dry unit weight, porosity, P‐wave velocity, Brinell surface harnesses, UCS, and static E were measured for 70 carbonate rock samples. Then, two stacking ensemble learning models were developed for estimating the UCS and E of the rocks. The applied stacking ensemble learning method integrates the advantages of two base models in the first level, where base models are multi-layer perceptron (MLP) and random forest (RF) for predicting UCS, and support vector regressor (SVR) and extreme gradient boosting (XGBoost) for predicting E. Grid search integrating k-fold cross validation is applied to tune the parameters of both base models and meta-learner. The results demonstrate the generalization ability of the stacking ensemble method in the comparison of base models in the terms of common performance measures. The values of coefficient of determination (R2) obtained from the stacking ensemble are 0.909 and 0.831 for predicting UCS and E, respectively. Similarly, the stacking ensemble yielded Root Mean Squared Error (RMSE) values of 1.967 and 0.621 for the prediction of UCS and E, respectively. Accordingly, the proposed models have superiority in the comparison of SVR and MLP as single models and RF and XGBoost as two representative ensemble models. Furthermore, sensitivity analysis is carried out to investigate the impact of input parameters.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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“…Integrated learning methods are used to obtain better predictions by fusing multiple base learners and combining them [22]. Boosting is the most dominant class of integrated learning methods.…”

Section: Xgboost (Extreme Gradient Boosting)mentioning

confidence: 99%

Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost

Wei,

Ji,

et al. 2023

Applied Sciences

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Steel bridges are widely used in bridge engineering. In the structural design of steel bridge deck pavement systems, engineers focus on obtaining mechanical properties by calculating design parameters and are keen to establish a quick and accurate solution method. Because of the complex knowledge system involved in the numerical calculation method, it is difficult for the general engineering designer to master it. Researchers have started using artificial intelligence algorithms to solve problems in civil engineering. This study developed an XGBoost-based mechanical performance prediction model for steel bridge deck pavement systems. First, numerical simulation tests are conducted at unfavorable load locations using a finite element model to establish a dataset. Then, an XGBoost model is built using this dataset, and its parameters are optimized and compared with traditional machine learning models. Finally, an explanatory analysis of the model is performed using SHAP, an interpretable machine learning framework. The results indicate that the developed XGBoost model accurately predicts the mechanical properties of steel bridge deck pavement systems.

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“…The Doppler effect was first discovered by the Austrian physicist J. Doppler in 1842 from a moving sound source (Ghaemi et al, 2022). The working principle of Doppler weather radar is based on the Doppler effect.…”

Section: Threshold Determination Of the Lightning Warning Factormentioning

confidence: 99%

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

Wan,

Fu,

et al. 2023

Front. Energy Res.

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Lightning is one of the frequent natural disasters, which seriously affects the secure and stable operation of the power system, especially the distribution network lines with weak reliability. In order to improve the power supply reliability of the distribution network, higher requirements are put forward for the accuracy of lightning warning. Therefore, this paper establishes a lightning warning model based on comprehensive multi-meteorological factor thresholds and analyzes the meteorological factor data such as atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content under thunderstorm weather. The threshold value of each factor warning is obtained, and the corresponding threshold weight is calculated by the entropy weight method. According to the weight of each threshold, the comprehensive threshold index of lightning warning is obtained, and the lightning warning is based on this index. A total of 105 lightning data from May to June 2022 in Nanchang city were analyzed as samples. The thresholds of atmospheric field strength, echo intensity, echo-top height, and vertical cumulative liquid water content were 1.2 kV/m, 40 dBZ, 8 km, and 5.2 kg·m−2, respectively. The corresponding weights of each factor were 0.4188, 0.2056, 0.2105, and 0.165, respectively. This model was used to warn a thunderstorm event in July 2022 in Nanchang area. The success rate of the model warning was 0.91, the false alarm rate (FAR) was 0.11, and the critical success index (CSI) was 0.80. Compared with the single-factor threshold lightning warning model, the warning FAR is decreased by 6%, and CSI is increased by 14% while ensuring the high warning success rate.

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Situational awareness and deficiency warning system in a smart distribution network based on stacking ensemble learning

Cited by 11 publications

References 40 publications

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost

Optimization of the lightning warning model for distribution network lines based on multiple meteorological factor thresholds

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