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Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method.

Détection d’une consommation énergétique anormale pour un système de pompe à chaleur géothermique basée sur l’apprentissage approfondi associé à une méthode de modélisation statistique.

Auteurs : XU C., CHEN H.

Type d'article : Article de la RIF

Résumé

Energy consumption of heat pump system accounts for a large part of the total building energy consumption, and the energy-saving operation of heat pump system has always been the focus of researchers. A promising solution to tackling energy wastes during system operations is anomaly detection. In this study, we propose an anomaly detection method for GSHP system in a public building based on mode decomposition based LSTM and statistical modeling method Grubbs’ test. The system energy consumption is predicted using mode decomposition based LSTM algorithm, and the difference between predicted value and actual value is used to detect the abnormal system energy consumption by Grubbs’ test. Results show that detected anomalies can be summarily divided into three categories (parabola anomaly, abrupt anomaly and time related anomaly) depending on their characteristics, and the rationality of detected anomalies are evaluated through field investigation and expert knowledge. This work is enlightening and indicates that the proposed method would efficiently detect the abnormal performance of GSHP system, and find out unreasonable operating patterns.

Documents disponibles

Format PDF

Pages : 106-117

Disponible

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    15 €

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Détails

  • Titre original : Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method.
  • Identifiant de la fiche : 30027451
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 114
  • Date d'édition : 06/2020
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2020.02.035

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