摘要：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 sufcient 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 diferent imputation methods . The original and processed data are published together with the code for data processing (https://doi.org/10.5281/zenodo.6563114) .
