Remaining Useful Life Prediction of PV Systems Under Dynamic Environmental Conditions-Reference-Cited by-同舟云学术

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Remaining Useful Life Prediction of PV Systems Under Dynamic Environmental Conditions

Published:2023-07 Issue:4 Volume:13 Page:590-602
ISSN:2156-3381
Container-title:IEEE Journal of Photovoltaics
language:
Short-container-title:IEEE J. Photovoltaics

Author:

Liu Qifang¹^ORCID,Hu Qingpei¹^ORCID,Zhou Jinfeng²^ORCID,Yu Dan¹,Mo Huadong³

Affiliation:

1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

2. China Biodiversity Conservation and Green Development Foundation, Beijing, China

3. School of Engineering and Information Technology, University of New South Wales, Sydney, NSW, Australia

Funder

National Key R&D Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/5503869/10192919/10123306.pdf?arnumber=10123306

Reference57 articles.

1. Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module

2. Individualized Degradation Modeling and Prognostics in a Heterogeneous Group via Incorporating Intrinsic Covariate Information

3. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

4. Degradation in Common Dynamic Environments

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1. Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives;Renewable and Sustainable Energy Reviews;2024-11

2. Photovoltaic systems operation and maintenance: A review and future directions;Renewable and Sustainable Energy Reviews;2024-05

3. Multiphase Solar Photovoltaic Prediction Model Based on Season, Hierarchical k‐Means Clustering, GRA‐PCC, SVM, and Neural Network;International Journal of Energy Research;2024-01

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