Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings

The now widespread use of smart heat meters for buildings connected to district heating networks generates data at an unknown extent and temporal resolution. This data encompasses information that enables new data-driven approaches in the building sector. Real-life data of sufficient size and quality are necessary to facilitate the development of such methods, as subsequent analyses typically require a complete equidistant dataset without missing or erroneous values. Thus, this work presents three years (2018-01-03 till 2020-12-31) of screened, interpolated, and imputed data from 3,021 commercial smart heat meters installed in Danish residential buildings. The screening aimed to detect data from not used meters, resolve issues caused by the data storage process and identify erroneous values. Linear interpolation was used to obtain equidistant data. After the screening, 0.3% of the data were missing, which were imputed using a weighted moving average based on a systematic comparison of nine different imputation methods. The original and processed data are published together with the code for data processing (10.5281/zenodo.6563114)

Who Produces the Peaks? Household Variation in Peak Energy Demand for Space Heating and Domestic Hot Water

Extensive research demonstrates the importance of user practices in understanding variations in residential heating demand. Whereas previous studies have investigated variations in aggregated data, e.g., yearly heating consumption, the recent deployment of smart heat meters enables the analysis of households’ energy use with a higher temporal resolution. Such analysis might provide knowledge crucial for managing peak demand in district heating systems with decentralized production units and increased shares of intermittent energy sources, such as wind and solar. This study exploits smart meter heating consumption data from a district heating network combined with socio-economic information for 803 Danish households. To perform this study, a multiple regression analysis was employed to understand the correlations between heat consumption and socio-economical characteristics. Furthermore, this study analyzed the various households’ daily profiles to quantify the differences between the groups. During an average day, the higher-income households consume more energy, especially during the evening peak (17:00–20:00). Blue-collar and unemployed households use less during the morning peak (5:00–9:00). Despite minor differences, household groups have similar temporal patterns that follow institutional rhythms, like working hours. We therefore suggest that attempts to control the timing of heating demand do not rely on individual households’ ability to time-shift energy practices, but instead address the embeddedness in stable socio-temporal structures

Influence of Temporal- and Spatial Resolutions on Building Performance Simulation Models: A Danish Residential Building Case Study

This study aims to assess the accuracy of a building performance simulation (BPS) model developed in IDA ICE software, focusing on heating energy use and indoor air temperatures in a low-energy multi-story residential building located in Northern Denmark. Six apartments were analyzed, and a comparative analysis was conducted between the measured parameters and the results obtained from BPS models with different spatial and temporal resolutions. The findings indicate that while the BPS models can provide reasonably accurate estimates of heating energy use, they may not fully capture the nuanced response to factors such as indoor air temperature This highlights the importance of incorporating qualitative inputs and environmental variables into these BPS models, including heating and/or cooling setpoints, internal gains, and weather conditions. Overall, this study provides insights into the limitations and opportunities of BPS models for accurately estimating heating energy use and indoor air temperatures in low-energy residential buildings

Treatment and analysis of smart energy meter data from a cluster of buildings connected to district heating:A Danish case

District heating has been found to be a key component of future and reliable smart energy grids comprising 100% of renewable energy sources for countries with dominant heating season. However, these systems face challenges that require a deeper understanding of the coupling between the distribution networks and the connected buildings, to enable demand-side management and balance the intermittence of renewables. In recent years, many smart energy meters have been installed on the heating systems of Danish dwellings connected to district heating, and the first yearly measurement data sets of large building clusters are now available. This article presents the methodology for the pre-processing and cluster analysis (K-means clustering) of a one-year-long smart energy meter measurement data from 1665 Danish dwellings connected to district heating. The aim is to identify typical household daily profiles of heat energy use, return temperature, and temperature difference between the supply and the return fluid. The study is performed with the free software environment “R”, which enables the rapid extraction of information to be shared with professionals of the building and energy sectors. After presenting the preliminary results of the clustering analysis, the article closes with the future work to be conducted on this study case

Analysing energy use clusters of single-family houses using building and socio-economic characteristics

Clustering has been shown to be a promising approach to reduce the large amount of data from smart heat meters to representative profiles. However, attempts to understand why a case (building including its occupants) is within a particular cluster have only been moderately accurate. Therefore, this work uses existing energy use clusters based on about 4500 single-family homes to investigate whether socio-economic characteristics (SECs) alone or in combination with building characteristics (BCs) can improve the insight into the energy use clusters. An established variable selection and classification approach based on random forests was used. The results show that the eight SECs used alone provide poor insight into the energy use clusters, achieving only a Matthew Correlation Coefficient (MCC) of around 0.1. Simplifying the energy use clusters based on similarities, which was successful in the past, only moderately increased the MCC (≈ 0.17). When combined with BCs, SECs were never selected by the algorithm used, showing that they do not lead to a (significant) increase in MCC for both unsimplified and simplified clusters. Thus, this work suggests that SECs do not provide additional insights into why a case is within its respective energy use cluster