Detection of heat pumps from smart meter and open data

Energy Informatics

Konstantin Hopf¹  Nicolai Weinig¹  Andreas Weigert¹  Thorsten Staake² 

[1] Information Systems and Energy Efficient Systems, University of Bamberg, Kapuzinerstraße 16, DE-96047, Bamberg, Germany;Information Systems and Energy Efficient Systems, University of Bamberg, Kapuzinerstraße 16, DE-96047, Bamberg, Germany;Department of Management, Technology and Economics, ETH Zurich, Weinbergstrasse 5, CH-8092, Zurich, Switzerland;

关键词: Heat pump detection;    Smart meter data;    Machine learning;   

DOI  :  10.1186/s42162-020-00124-6

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY