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

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.

Data Description Paper

For a detailed explanation of the dataset structure, file contents, and parameters, please refer to the dataset description paper: https://dl.acm.org/doi/full/10.1145/3679240.3734637 If you use the dataset, please cite our paper as follows:

@inproceedings{10.1145/3679240.3734637,
author = {Brudermueller, Tobias and Fleisch, Elgar and Vay\'{a}, Marina Gonz\'{a}lez and Staake, Thorsten},
title = {HEAPO – An Open Dataset for Heat Pump Optimization with Smart Electricity Meter Data and On-Site Inspection Protocols},
year = {2025},
isbn = {9798400711251},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3679240.3734637},
doi = {10.1145/3679240.3734637},
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.},
booktitle = {Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems},
pages = {699–711},
numpages = {13},
keywords = {heat pump, optimization, smart electricity meter, open energy data},
location = {},
series = {E-Energy '25}
}

Code Availability

A Python-based dataloader and data usage instructions can be found on GitHub: https://github.com/tbrumue/heapo

Data Download

The data is available for download on Zenodo: https://zenodo.org/records/15056919