Detection of heat pumps from smart meter and open data
Abstract Heat pumps embody solutions that heat or cool buildings effectively and sustainably, with zero emissions at the place of installation. As they pose significant load on the power grid, knowledge on their existence is crucial for grid operators, e.g., to forecast load and to plan grid operati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-10-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s42162-020-00124-6 |
| _version_ | 1818404071682342912 |
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| author | Andreas Weigert Konstantin Hopf Nicolai Weinig Thorsten Staake |
| author_facet | Andreas Weigert Konstantin Hopf Nicolai Weinig Thorsten Staake |
| author_sort | Andreas Weigert |
| collection | DOAJ |
| description | Abstract Heat pumps embody solutions that heat or cool buildings effectively and sustainably, with zero emissions at the place of installation. As they pose significant load on the power grid, knowledge on their existence is crucial for grid operators, e.g., to forecast load and to plan grid operation. Further details, like the thermal reservoir (ground or air source) or the age of a heat pump installation renders energy-related services possible that utility companies can offer in the future (e.g., detecting wrongly calibrated installations, household energy efficiency checks). This study investigates the prediction of heat pump installations, their thermal reservoir and age. For this, we obtained a dataset with 397 households in Switzerland, all equipped with smart meters, collected ground truth data on installed heat pumps and enriched this data with weather data and geographical information. Our investigation replicates the state of the art in the area of heat pump detection and goes beyond it, as we obtain three major findings: First, machine learning can detect the existence of heat pumps with an AUC performance metric of 0.82, their heat reservoir with an AUC of 0.86, and their age with an AUC of 0.73. Second, heat pump existence can be better detected using data during the heating period than during summer. Third the number of training samples to detect the existence of heat pumps must not be necessarily large in terms of the number of training instances and observation period. |
| first_indexed | 2024-12-14T08:34:19Z |
| format | Article |
| id | doaj.art-abdf24e04a5c4b6cacaaf53642f55da4 |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-12-14T08:34:19Z |
| publishDate | 2020-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-abdf24e04a5c4b6cacaaf53642f55da42022-12-21T23:09:27ZengSpringerOpenEnergy Informatics2520-89422020-10-013S111410.1186/s42162-020-00124-6Detection of heat pumps from smart meter and open dataAndreas Weigert0Konstantin Hopf1Nicolai Weinig2Thorsten Staake3Information Systems and Energy Efficient Systems, University of BambergInformation Systems and Energy Efficient Systems, University of BambergInformation Systems and Energy Efficient Systems, University of BambergInformation Systems and Energy Efficient Systems, University of BambergAbstract Heat pumps embody solutions that heat or cool buildings effectively and sustainably, with zero emissions at the place of installation. As they pose significant load on the power grid, knowledge on their existence is crucial for grid operators, e.g., to forecast load and to plan grid operation. Further details, like the thermal reservoir (ground or air source) or the age of a heat pump installation renders energy-related services possible that utility companies can offer in the future (e.g., detecting wrongly calibrated installations, household energy efficiency checks). This study investigates the prediction of heat pump installations, their thermal reservoir and age. For this, we obtained a dataset with 397 households in Switzerland, all equipped with smart meters, collected ground truth data on installed heat pumps and enriched this data with weather data and geographical information. Our investigation replicates the state of the art in the area of heat pump detection and goes beyond it, as we obtain three major findings: First, machine learning can detect the existence of heat pumps with an AUC performance metric of 0.82, their heat reservoir with an AUC of 0.86, and their age with an AUC of 0.73. Second, heat pump existence can be better detected using data during the heating period than during summer. Third the number of training samples to detect the existence of heat pumps must not be necessarily large in terms of the number of training instances and observation period.http://link.springer.com/article/10.1186/s42162-020-00124-6Heat pump detectionSmart meter dataMachine learning |
| spellingShingle | Andreas Weigert Konstantin Hopf Nicolai Weinig Thorsten Staake Detection of heat pumps from smart meter and open data Energy Informatics Heat pump detection Smart meter data Machine learning |
| title | Detection of heat pumps from smart meter and open data |
| title_full | Detection of heat pumps from smart meter and open data |
| title_fullStr | Detection of heat pumps from smart meter and open data |
| title_full_unstemmed | Detection of heat pumps from smart meter and open data |
| title_short | Detection of heat pumps from smart meter and open data |
| title_sort | detection of heat pumps from smart meter and open data |
| topic | Heat pump detection Smart meter data Machine learning |
| url | http://link.springer.com/article/10.1186/s42162-020-00124-6 |
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