Short-term anomaly detection in gas consumption through ARIMA and Artificial Neural Network forecast

Authors
Publication date 2015
Book title 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems
Book subtitle July 9th-10th, 2015, Department of Sociology, Room Kessler, via Verdi 26, Trento, Italy : proceedings
ISBN
  • 9781479982141
ISBN (electronic)
  • 9781479982158
Event IEEE Workshop on Environmental, Energy and Structural Monitoring Systems (EESMS 2015)
Pages (from-to) 250-255
Publisher Piscataway, NJ: IEEE
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract This paper presents a method for finding anomalies in gas consumption that can identify causes of wasting energy. Our approach is to use historical data on local weather, building usage and gas consumption, to predict the gas consumption for a particular day and time. The prediction is a combination of auto-regression and artificial neural networks and anomalies, relatively large deviations from the predicted gas consumption values, are detected. These can point to incorrect settings of controls, faults in installations or incorrect use of the building.
Document type Conference contribution
Language English
Published at https://doi.org/10.1109/EESMS.2015.7175886
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