Heat pump inspections result in large energy savings when a pre-selection of households is performed : A promising use case of smart meter data

Faculty/Professorship: Information Systems and Energy Efficient Systems  
Author(s): Weigert, Andreas  ; Hopf, Konstantin  ; Günther, Sebastian A.; Staake, Thorsten
Title of the Journal: Energy Policy
ISSN: 03014215
Publisher Information: Amsterdam [u.a.] : Elsevier
Year of publication: 2022
Volume: 169
Issue: October
Pages: 1-15
Language(s): English
DOI: 10.1016/j.enpol.2022.113156
Heat pumps play an important role in the energy transition. They can extract renewable energy from the air or ground and increasingly replace fossil heating systems in buildings. In operation, however, heat pumps often consume more electricity than necessary due to incorrect settings and installation deficiencies. Although many setting and installation issues are easy to fix, problems often go unnoticed, and the saving potential from quick fixes remains unclear. In a study with 297 Swiss households (41 treatment, 256 control) running for four years, we investigated an energy efficiency campaign in which the treatment group received a professional heat pump inspection and user training. We found considerable heterogeneity with respect to the savings achieved. We derived two criteria based on smart meter data that enable utilities to identify relevant households and thus boost the impact of such efficiency campaigns: For example, pre-selecting half of the households based on available information results in average savings of 1,805 kWh (15.2%) per year and household in the high-potential group compared to no savings in the low-potential group. Thus, heat pump inspections among pre-selected households can lead to large, cost-effective electricity savings, and we show that common smart meter data makes such pre-selection feasible.
GND Keywords: Wärmepumpe; Energieeffizienz; Energieberatung; Intelligenter Zähler; Datenanalyse
Keywords: Heat pump, Energy consulting, Energy efficiency, Smart meter data, Data analytics
DDC Classification: 333.7 Natural ressources, energy, environment  
RVK Classification: ST 530   
Peer Reviewed: Ja
International Distribution: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/55315
Release Date: 26. August 2022
Project: Automatische Erkennung von Effizienz- und Selbstversorgungspotenzialen individueller Haushalte