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Energy Anomaly Detection in Industrial Applications with Long Short-term Memory-based Autoencoders

Title data

Kaymakci, Can ; Wenninger, Simon ; Sauer, Alexander:
Energy Anomaly Detection in Industrial Applications with Long Short-term Memory-based Autoencoders.
2021
Event: 54th Conference on Manufacturing Systems (CIRP) , Virtual.
(Conference item: Conference , Speech )

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Nachhaltiges Energiemanagement & MobilitätNo information
Projektgruppe WI Digital FinanceNo information

Abstract in another language

With the goal of reducing energy costs, carbon emissions, and achieving cleaner production, manufacturing companies aim to reduce their energy consumption. In manufacturing companies, a considerable amount of energy is wasted due to plant-, process- and human-related faults. Tools and methods for detecting anomalies are widely used for fraud detection in finance or intrusion detection in cybersecurity. When it comes to anomaly detection of malicious energy consumption, the residential building sector is leading. Industrial applications are not being addressed by now. In this paper, an end-to-end solution of an anomaly detection system is presented that uses the concept of a Long Short-term Memory based Autoencoder (LSTM-AE) as an unsupervised learning model that detects anomalies without labeling the data beforehand.

Further data

Item Type: Conference item (Speech)
Refereed: Yes
Keywords: Anomaly detection; Long-Short-Term-Memory; Autoencoder; Energy consumption; AI in manufacturing systems
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 07 Sep 2021 07:24
Last Modified: 20 Oct 2021 09:55
URI: https://eref.uni-bayreuth.de/id/eprint/66943