Abstract
Diagnosing abnormal behavior of different severity and convenience effects in a real-time manner is of paramount importance for energy-intensive building appliances. Both industrial and residential sectors suffer from post-incident maintenance where undetected faults occur for several days until the total breakdown of the equipment. To generate the necessary data set, a simulative test bed from Energym initiative was considered, exploiting an already validated residential environment. In this work, a Convolutional Neural Network (CNN) model was considered for classifying non-intrusive, low-cost temperature sensor embeddings in 3 categories with different abnormal heat pump severity levels. The features considered available derived from indoor zones temperatures and the outdoor/ambient temperature of the building; omitting intentionally readings from more elaborate sensors e.g., power analyzers or energy meters. The trained CNN model was eventually able to achieve very high accuracy i.e., around 95%; ensuring its high operational reliability by consuming real-time 15 min sequential temperature embeddings.
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Acknowledgements
The research leading to these results was partially funded by the European Commission “LC-EEB-07-2020 - Smart Operation of Proactive Residential Buildings” - PRECEPT H2020 project (Grant agreement ID: 958284) https://www.precept-project.eu/, accessed on 8 March 2022; and “LC-SC3-B4E-3-2020 Upgrading smartness of existing buildings through innovations for legacy equipment” - Smart2B H2020 project (Grant agreement ID: 101023666) https://www.smart2b-project.eu/, accessed on 2 March 2022.
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Michailidis, I. et al. (2022). Non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_22
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DOI: https://doi.org/10.1007/978-3-031-08341-9_22
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