@article{LEIRIA2021100035,
title = {Using data from smart energy meters to gain knowledge about households connected to the district heating network: A Danish case},
journal = {Smart Energy},
volume = {3},
pages = {100035},
year = {2021},
issn = {2666-9552},
doi = {https://doi.org/10.1016/j.segy.2021.100035},
url = {https://www.sciencedirect.com/science/article/pii/S2666955221000356},
author = {Daniel Leiria and Hicham Johra and Anna Marszal-Pomianowska and Michal Zbigniew Pomianowski and Per {Kvols Heiselberg}},
keywords = {District heating, Smart energy meters, Big data mining, Linear regression analysis, Energy performance certificates, Building characterization},
abstract = {In Europe, one of the most sustainable solutions to supply heat to buildings is district heating. It has good acceptance in the Northern countries, a low-carbon footprint, and can easily integrate intermittent renewable energy sources when coupled to the electrical grid. Even though district heating is seen as a vital element for a sustainable future, it requires extensive planning and long-term investments. To increase the understanding of the district heating network performance and the demand-side dynamics of the connected buildings, several countries, including Denmark, have installed smart heat meters in different cities. In that context, this paper presents several methodologies to analyze the datasets from the smart heat meters installed in a small Danish town. The first method is concerning data curation to remove the anomalies and missing data points. The second method analyses measured variables (heat consumption, outdoor temperature, wind speed, and global radiation) to acquire new knowledge on the building characteristics. These results were compared with the values given by the energy performance certificates of a smaller sample of 41 households. Finally, to communicate and visualize the analysis outputs in a user-friendly way, an interactive web interface tool has been created.}
}