Increasing power plant efficiency with clustering methods and Variable Importance Index assessment-Reference-Cited by-同舟云学术

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Increasing power plant efficiency with clustering methods and Variable Importance Index assessment

Published:2021-09 Issue: Volume:5 Page:100084
ISSN:2666-5468
Container-title:Energy and AI
language:en
Short-container-title:Energy and AI

Author:

Duarte Jéssica,Vieira Lara Werncke^ORCID,Marques Augusto Delavald^ORCID,Schneider Paulo Smith,Pumi Guilherme,Prass Taiane Schaedler

Funder

FAPERGS

CNPq

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Artificial Intelligence,General Energy,Engineering (miscellaneous)

Reference27 articles.

1. World energy outlook 2018 executive summary;IEA,2018

2. An introduction to statistical learning;James,2013

3. Detection of boiler tube leakage fault in a thermal power plant using machine learning based data mining technique;h. Kim,2019

4. A mixed intelligent condition monitoring method for nuclear power plant;Peng;Anna Nucl Energy,2020

5. Early warning method for power station auxiliary failure considering large-scale operating conditions;Tingting,2019

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Uncovering Insights in Coal Mill Plant Operation Modes Using GMM-LSTM-Based Unsupervised Clustering;2024 32nd Southern African Universities Power Engineering Conference (SAUPEC);2024-01-24

2. An Interpretable Model for the Prediction of Performance Parameters in Thermal Power Plants Utilizing Expert Knowledge and Machine Learning;2024

3. Extracting the Extreme Mode Operation of Coal Mills Using the Gaussian Mixture Model Method;2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA);2023-01-29

4. Strategic-level performance enhancement of a 660 MWe supercritical power plant and emissions reduction by AI approach;Energy Conversion and Management;2021-12

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