Large-scale monitoring of residential heat pump cycling using smart meter data

Tobias Brudermueller, Markus Kreft, Elgar Fleisch and Thorsten Staake

Applied Energy, 2023, vol. 350, issue C, No S030626192301098X

Abstract: Heat pumps play an essential role in decarbonizing the building sector, but their electricity consumption can vary significantly across buildings. This variability is closely related to their cycling behavior (i.e., the frequency of on–off transitions), which is also an indicator for improper sizing and non-optimal settings and can affect a heat pump’s lifetime. Up to now it has been unclear which cycling behaviors are typical and atypical for heat pump operation in the field and importantly, there is a lack of methods to identify heat pumps that cycle atypically. Therefore, in this study we develop a method to monitor heat pumps with energy measurements delivered by common smart electricity meters, which also cover heat pumps without network connectivity. We show how smart meter data with 15-minute resolution can be used to extract key indicators about heat pump cycling and outline how atypical behavior can be detected after controlling for outdoor temperature. Our method is robust across different building characteristics and varying times of observation, does not require contextual information, and can be implemented with existing smart meter data, making it suitable for real-world applications. Analyzing 503 heat pumps in Swiss households over a period of 21 months, we further describe behavioral differences with respect to building and heat pump characteristics and study the relationship between heat pumps’ cycling behavior, energy efficiency, and appropriate sizing. Our results show that outliers in cycling behavior are more than twice as common for air-source heat pumps than for ground-source heat pumps.

Keywords: Smart meter data; Heat pump; Cycling behavior; outlier detection; Residential buildings; Energy efficiency; Appliance monitoring; Real-world operation; Machine learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:350:y:2023:i:c:s030626192301098x

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DOI: 10.1016/j.apenergy.2023.121734

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