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EnglishThis 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.
Short-term anomaly detection in gas consumption through ARIMA and Artificial Neural Network forecast / De Nadai, Marco; Van Someren, Maarten. - (2015), pp. 250-255. ((Intervento presentato al convegno 7th IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 tenutosi a Trento nel 9th-10th Jul 2015.
| Titolo: | Short-term anomaly detection in gas consumption through ARIMA and Artificial Neural Network forecast |
| Autori: | De Nadai, Marco; Van Someren, Maarten |
| Autori Unitn: | |
| Titolo del volume contenente il saggio: | 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems: Proceedings |
| Luogo di edizione: | Piscataway, NJ |
| Casa editrice: | Institute of Electrical and Electronics Engineers Inc. |
| Anno di pubblicazione: | 2015 |
| Codice identificativo Scopus: | 2-s2.0-84951038865 |
| Codice identificativo ISI: | WOS:000380429500043 |
| ISBN: | 9781479982141 |
| Handle: | http://hdl.handle.net/11572/126574 |
| Citazione: | Short-term anomaly detection in gas consumption through ARIMA and Artificial Neural Network forecast / De Nadai, Marco; Van Someren, Maarten. - (2015), pp. 250-255. ((Intervento presentato al convegno 7th IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015 tenutosi a Trento nel 9th-10th Jul 2015. |
| Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |
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http://hdl.handle.net/11572/126574
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