A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data-Reference-Cited by-同舟云学术

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A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data

Published:2022-11 Issue: Volume:225 Page:109641
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Zhang Qiang,Tian Zhe,Niu Jide,Zhu Jie,Lu Yakai

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference46 articles.

1. A short-term building cooling load prediction method using deep learning algorithms;Fan;Appl. Energy,2017

2. A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems;Li;Appl. Energy,2022

3. A knowledge-guided and data-driven method for building HVAC systems fault diagnosis;Li;Build. Environ.,2021

4. A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings;Zhang;Build. Environ.,2020

5. An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network;Zhao;Energy Build.,2013

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