Li, Zhangling https://orcid.org/0009-0007-8356-0263
Wang, Qi https://orcid.org/0000-0002-6817-7967
Xiong, Jianbin https://orcid.org/0000-0002-2253-5546
Cen, Jian https://orcid.org/0000-0002-1714-7397
Dai, Qingyun https://orcid.org/0000-0002-7561-3704
Liang, Qiong
Lu, Tiantian https://orcid.org/0000-0002-1328-4984
Funding for this research was provided by:
National Natural Science Foundation of China-Guangdong Joint Fund (U22A20221)
National Natural Science Foundation of China (62073090)
Key (natural) Project of Guangdong Province (2020ZDZX2014)
Introduction of Talents Project of Guangdong Polytechnic Normal University of China (991512203)
Guangdong Provincial Key Laboratory Project of Intellectual Property and Big Data (2018B030322016)
Natural Science Foundation of Guangdong Province (2023A1515011423)
Article Title: A building electrical system fault diagnosis method based on random forest optimized by improved sparrow search algorithm
Journal Title: Measurement Science and Technology
Article Type: paper
Copyright Information: © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Publication dates
Date Received: 2023-08-30
Date Accepted: 2024-01-24
Online publication date: 2024-02-14