HEAPO – An Open Dataset for Heat Pump Optimization with Smart Electricity Meter Data and On-Site Inspection Protocols

Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand.Many systems run inefficiently due to planning flaws, operational faults, or misconfigurations.While optimizing performance requires skilled professionals, labor shortages hinder large-scale interventions.However, digital tools and improved data availability create new service opportunities for energy efficiency, predictive maintenance, and demand-side management.To support research and practical solutions, we present an open-source dataset of electricity consumption from 1,408 households with heat pumps and smart electricity meters in the canton of Zurich, Switzerland, recorded at 15-minute and daily resolutions between 2018-11-03 and 2024-03-21.The dataset includes household metadata, weather data from 8 stations, and ground truth data from 410 field visit protocols collected by energy consultants during system optimizations.Additionally, the dataset includes a Python-based data loader to facilitate seamless data processing and exploration.

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Publication Details

Published
2025-06-16
DOI
https://doi.org/10.1145/3679240.3734637
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.73

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article

HEAPO – An Open Dataset for Heat Pump Optimization with Smart Electricity Meter Data and On-Site Inspection Protocols

Tobias Brudermueller, Thorsten Staake, Elgar Fleisch, Marina González Vayá
1 citations
Smart Grid Energy Management
0.73
article

HEAPO – An Open Dataset for Heat Pump Optimization with Smart Electricity Meter Data and On-Site Inspection Protocols

Tobias Brudermueller, Thorsten Staake, Elgar Fleisch, Marina González Vayá
article en
1 citations

Abstract

Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand.Many systems run inefficiently due to planning flaws, operational faults, or misconfigurations.While optimizing performance requires skilled professionals, labor shortages hinder large-scale interventions.However, digital tools and improved data availability create new service opportunities for energy efficiency, predictive maintenance, and demand-side management.To support research and practical solutions, we present an open-source dataset of electricity consumption from 1,408 households with heat pumps and smart electricity meters in the canton of Zurich, Switzerland, recorded at 15-minute and daily resolutions between 2018-11-03 and 2024-03-21.The dataset includes household metadata, weather data from 8 stations, and ground truth data from 410 field visit protocols collected by energy consultants during system optimizations.Additionally, the dataset includes a Python-based data loader to facilitate seamless data processing and exploration.

University of St.Gallen (CH), ETH Zurich (CH), University of Bamberg (DE)
Bundesamt für Energie
Affordable and clean energy
Openalex Percentile: Top 27%
Smart Grid Energy Management
0.73
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