Interpretable machine learning for battery capacities prediction and coating parameters analysis

Liu, Kailong; Niri, Mona Faraji; Apachitei, Geanina; Lain, Michael; Greenwood, David; Marco, James

doi:10.1016/j.conengprac.2022.105202

Cited by 50 publications

(32 citation statements)

References 47 publications

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“…After the BIC characteristic treatment, the speed, trim, draft, port pitch, starboard pitch, port rudder, starboard rudder, and head wind were retained. In addition, a RF is used to quantitatively calculate the importance of features [42,43], and the results are shown in Figure 5. As shown in the fgure, starboard pitch, port pitch, speed, trim, and headwind have high feature importance values, indicating that they are of high importance to energy consumption modeling, which is consistent with the results of the BIC method.…”

Section: Resultsmentioning

confidence: 99%

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Gao

Chi

2022

Mathematical Problems in Engineering

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Global warming, environmental pollution, and the soaring cost of energy consumption for ships have drawn the attention of the International Maritime Organization and the shipping industry. By reducing the energy consumption of ships, the greenhouse gas emissions and operating costs of ships can be effectively reduced simultaneously. However, current research on the ship energy consumption optimization based on operating mode is mainly focused on route and speed optimization and less on trim optimization, but ship trim is also an important factor affecting energy consumption. Therefore, this study proposed a ship trim optimization method based on operational data and ensemble learning to achieve energy savings and emission reductions for inland sea ships. First, data processing and feature selection of operational data from an inland ro-ro passenger ship were undertaken. Second, the energy consumption prediction models were established based on ensemble learning. Finally, the trim optimization model was developed by combining the energy consumption model with the best prediction performance and enumeration method. Experimental results show that compared with linear regression, neural networks, and support vector machines, ensemble learning methods have better prediction performance in energy consumption modeling, and the extra tree (ET) model has the best prediction performance. With the trim optimization, the energy consumption and carbon emissions of a ro-ro passenger ship can be reduced by 1.4641%, which is conducive to the green and low-carbon navigation of ships.

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Section: Resultsmentioning

confidence: 99%

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Gao

Chi

2022

Mathematical Problems in Engineering

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“…Only time series that pass the cointegration test can be applied to the proposed approach. In addition, the existing deep machine learning methods, such as the combination of self-attention mechanisms and the data-driven method of the generic adversarial network, the ensemble machine learning method [28][29][30][31], will be used as references in the future research to closely combine the application conditions of ECM and develop more efective data-driven nonlinear ECM.…”

Section: Discussionmentioning

confidence: 99%

“…Interpretability is an important research direction for machine learning approaches. Liu et al [ 30 ] made extraordinary and meaningful contributions. An interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects is proposed which also provides interactions of manufacturing parameters.…”

Section: Introductionmentioning

confidence: 99%

A Neural Learning Approach for a Data‐Driven Nonlinear Error Correction Model

Fang

Yang

2023

Computational Intelligence and Neuroscience

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A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network to learn the nonlinear error correction model by combining a linear recurrent neural network with a multilayer BP network. The network learning algorithm is given by using the gradient descent method and error back propagation. Based on the principle of data-driven, all network parameters can be obtained through the network learning and training. The daily data of gold price and the US dollar index in 2021 were used to verify this proposed nonlinear ECM neural learning method and the results were compared by the likelihood ratio Chi-square test. Simulation results show that the proposed data-driven nonlinear error correction neural learning method can improve goodness of fit statistical significantly of complex nonlinear relationship between time series.

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“…As regression models are based on the assumption that observations are mutually independent and identically distributed, models based on parameter estimation are no longer suitable. Nonparametric modeling approaches, including some typical machine learning methods [18,19], are widely used to solve the medical state prediction issues. Gao et al [20] utilized fve machine learning models including logistic regression, random forest, LightGBM, XGBoost, and their ensemble model to early predict the occurrence of acute kidney injury (AKI) in the next 24, 48, and 72 h for ICU patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Subgroup State Prediction under Different Noise Levels Using MODWT and XGBoost

Zhao

Nie

2023

Journal of Healthcare Engineering

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In medical states prediction, the observations of different individuals are generally assumed to follow an identical distribution, whereas precision medicine has a rigorous requirement for accurate subgroup analysis. In this research, an aggregated method is proposed by means of combining the results generated from different subgroup models and is compared with the original method for different denoising levels as well as the prediction gaps. The results using real data demonstrate the effectiveness of the aggregated method exhibiting superior performance such as 0.95 in AUC, 0.87 in F1, and 0.82 in sensitivity, particularly for the denoising level that is set to be 2. With respect to the variable importance, it is shown that some variables such as heart rate and lactate arterial become more important when the denoising level increases.

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Interpretable machine learning for battery capacities prediction and coating parameters analysis

Cited by 50 publications

References 47 publications

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

A Neural Learning Approach for a Data‐Driven Nonlinear Error Correction Model

Subgroup State Prediction under Different Noise Levels Using MODWT and XGBoost

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