Results 11 to 20 of about 447 (169)

Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation [PDF]

Advanced Science
Lithium‐rich Nickel–Cobalt–Manganese (Li‐rich NCM) materials are promising cathodes for lithium‐ion batteries, where electrochemical performance is sensitive to primary particle size.
Benediktus Madika, Chaeyul Kang, JooSung Shim, Taemin Park, Jung Hyeon Moon, EunAe Cho, Seungbum Hong   +6 more
doaj   +3 more sources

Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting

Energies, 2020
Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem.
Yonggang Li, Yue Wang, Binyuan Wu
doaj   +2 more sources

Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere, 2020
The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction ...
Ting Peng, Xiefei Zhi, Yan Ji, Luying Ji, Ye Tian   +4 more
doaj   +2 more sources

Risk-Aware Optimal Dispatch of Resource Aggregators Integrating NGBoost-Based Probabilistic Renewable Forecasting and Bi-Level Building Flexibility Engagements

Engineering
Pressure has been introduced into power systems owing to the intermittent and uncertain nature of renewable energy. As a result, energy resource aggregators are emerging in the electricity market to realize sustainable and economic advantages through ...
Li Hangxin, Ir Professor Wang Shengwei
exaly   +4 more sources

Probabilistic Shear Strength Prediction for Deep Beams Based on Bayesian-Optimized Data-Driven Approach

Buildings, 2023
To ensure the safety of buildings, accurate and robust prediction of a reinforced concrete deep beam’s shear capacity is necessary to avoid unpredictable accidents caused by brittle failure.
Mao-Yi Liu, Zheng Li, Hang Zhang
doaj   +2 more sources

Imbalanced rock burst assessment using variational autoencoder-enhanced gradient boosting algorithms and explainability

Underground Space
We conducted a study to evaluate the potential and robustness of gradient boosting algorithms in rock burst assessment, established a variational autoencoder (VAE) to address the imbalance rock burst dataset, and proposed a multilevel explainable ...
Shan Lin, Zenglong Liang, Miao Dong, Hongwei Guo, Hong Zheng   +4 more
doaj   +2 more sources

Explainable AI-based optimization framework for sustainable fly ash concrete design [PDF]

Scientific Reports
Fly ash is widely recognized as an effective cement replacement material for reducing the environmental impact of concrete production while maintaining acceptable mechanical performance.
Morteza Khorshidi, Pourya Nejatipour, Amirehsan Teimortashlu, Giuseppe Oliveto, Ehsan Afaridegan   +4 more
doaj   +2 more sources

Novel hybrid machine learning-based prediction of building space heating load: a comprehensive study [PDF]

Scientific Reports
Space heating load is the main energy consumption agent for residential buildings. Its prediction is crucial for the efficient and cost-effective management of energy suppliers.
Ahmet Emir, Halil Ibrahim Topal, Suat Ozturk   +2 more
doaj   +2 more sources

Development and validation of predictive models for the hemodialysis adequacy index (Kt/V) using multiple linear regression and machine learning algorithms [PDF]

Frontiers in Digital Health
Background and objectiveKt/V is a widely used indicator for assessing hemodialysis (HD) adequacy, but conventional measurement methods require blood sampling and are impractical for large-scale or real-time use.
Le-le Li, Rui-feng Xu, Liang-liang Huang, Bing-ru Lu, Shu-ting Zhang, Ya Tuo, Hui-qing Liu, Rui Ren, Shu-fang Wang, Ming-xia Sun, Xiao-tong Zhang, Jian-rong Zhao, Lei Yu, Yan Meng   +13 more
doaj   +2 more sources

Deterioration Modeling of Pavement Performance in Cold Regions Using Probabilistic Machine Learning Method

Infrastructures
Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the ...
Zhen Liu, Xingyu Gu, Wenxiu Wu
doaj   +2 more sources