Application of machine learning to evaluating and remediating models for energy and environmental engineering-Reference-Cited by-同舟云学术

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Application of machine learning to evaluating and remediating models for energy and environmental engineering

Published:2022-08 Issue: Volume:320 Page:119286
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Chen Hao,Zhang Chao,Yu Haizeng,Wang Zhilin,Duncan Ian,Zhou Xianmin,Liu Xiliang,Wang Yu,Yang Shenglai

Funder

Natural Science Foundation of Beijing Municipality

U.S. Department of Energy

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference61 articles.

1. Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales;Gassar;Appl Energy,2021

2. Prospect of big data application in drilling engineering;Xue,2020

3. A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks;Hussain;Appl Energy,2017

4. Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study;Wang;Appl Energy,2021

5. Prediction of diffuse solar irradiance using machine learning and multivariable regression;Lou;Appl Energy,2016

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2. Energy pipeline degradation condition assessment using predictive analytics – challenges, issues, and future directions;Journal of Pipeline Science and Engineering;2024-09

3. Field application of near-borehole fracture acoustic imaging using a three-dimensional scanning inversion method;Geoenergy Science and Engineering;2024-09

4. Model for fracture conductivity considering particle size redistribution caused by proppant crushing;Geoenergy Science and Engineering;2024-09

5. Research on a drilling rate of penetration prediction model based on the improved chaos whale optimization and back propagation algorithm;Geoenergy Science and Engineering;2024-09

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