Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach-Reference-Cited by-同舟云学术

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Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach

Published:2024-01 Issue: Volume:353 Page:122157
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Gao Bixuan,Kong Xiangyu,Li Shangze,Chen Yi,Zhang Xiyuan,Liu Ziyu,Lv Weijia

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference65 articles.

1. Anomaly detection based on joint spatio-temporal learning for building electricity consumption;Kong;Appl Energy,2023

2. A practical feature-engineering framework for electricity theft detection in smart grids;Razavi;Appl Energy,2019

3. A stacked machine and deep learning-based approach for Analysing electricity theft in smart grids;Khan;IEEE Trans Smart Grid,2022

4. Electricity theft detection in AMI using customers’ consumption patterns;Jokar;IEEE Trans Smart Grid,2016

5. Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data;Fan;Appl Energy,2018

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