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Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Skellefteå Municipality, Sweden.ORCID iD: 0000-0002-5704-4667
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
2023 (English)In: Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, (EEEIC / I&CPS Europe 2023), Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Real-time anomaly detection in real-time energy consumption helps identify the technical and infrastructure issues and events that result in significant energy waste and provides the end users feedback to address the issues, which reduces drift operation costs and saves energy in the building. This study proposes deep learning reconstruction models to detect anomalies in daily energy consumption data for nine school buildings. We evaluated the performance of three proposed models, stacked RNN-LSTM autoencoder, CNN-LSTM autoencoder, and LSTM Variational Autoencoder (VAE), to learn the features of normal consumption in an unsupervised manner and detect anomalies based on reconstruction error. We used Exponential Moving Average (EMA) and static threshold to detect local and global anomalies. The experimental results demonstrate that the local CNN-LSTM autoencoder performs better than the local Stacked Autoencoder(AE), with RMSE values ranging between 8-13% for electricity and 11-19% for district heating compared to 12-17% and 15-34% resulting from AE model, respectively. Local LSTM-Variational Autoencoder (VAE) outperformed both methods, with RMSE 4-6% for electricity and 5-7% for district heating. LSTM-VAE trained model on grouped training datasets of schools with similar energy consumption and building profiles has improved the local model by lowering RMSE values to 2-3% for electricity and 3-4% in district heating.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023.
Keywords [en]
Anomaly Detection, Autoencoder, CNN-LSTM, electricity and district heating, Daily Consumption, RNN-LSTM, school buildings
National Category
Energy Systems Energy Engineering
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-101201DOI: 10.1109/EEEIC/ICPSEurope57605.2023.10194605Scopus ID: 2-s2.0-85168668252ISBN: 979-8-3503-4744-9 (print)ISBN: 979-8-3503-4743-2 (electronic)OAI: oai:DiVA.org:ltu-101201DiVA, id: diva2:1794210
Conference
2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, June 6-9, 2023
Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2023-10-14Bibliographically approved

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Shahid, Zahraa KhaisSaguna, SagunaÅhlund, Christer

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