Real-world application of machine-learning-based fault detection trained with experimental data

Gerrit Bode, Simon Thul, Marc Baranski and Dirk Müller

Energy, 2020, vol. 198, issue C

Abstract: Buildings are responsible for a large portion of the overall energy consumption. With the rising penetration of renewable energies, the heating and cooling demand of buildings will be increasingly satisfied by heat pumps. However, faults in the heat pump systems reduce energy efficiency or cause system failure, leading to an increased demand for primary energy. Hence, fault detection algorithms (FDA) are used to identify faults before system failure or efficiency deterioration occurs.

Keywords: Building automation and control; Fault detection; Heat pump; Machine learning; Big data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304308

DOI: 10.1016/j.energy.2020.117323

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