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)

Validating a Building Performance Simulation Model of a naturally ventilated Double Skin Facade

Double Skin Facades (DSF) are regaining popularity as a way to increase the climate resilience of buildings. Building Performance Simulation (BPS) is commonly used for their assessment, but modelling DSFs with BPS is challenging due to their complex thermophysical behaviour. Several research works have evaluated the capabilities and limitations of BPS for modelling specific DSF configurations. This work presents a validation study based on experimental data from a full-scale naturally ventilated double-skin façade, compared against results from the BPS software IDA-ICE. The study found that in periods with low solar irradiation, the different modelling strategies had a minor influence on the results, with a high agreement between measurements and simulation. In contrast, periods with solar irradiation showed a higher sensitivity to the modelling strategy, with more significant deviations from the measurement results

Estimating residential space heating and domestic hot water from truncated smart heat data

The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth–Pointwise Move–Scale) algorithm, which estimates decimal values from DH truncated measurements, to improve the accuracy of the DHW and SH disaggregation methods. The study applies these two methodologies to a dataset of 28 Danish apartments and compares the results against full-resolution and truncated data to evaluate performance. Another dataset, named "optimal dataset" is also assessed to determine overall estimation accuracy. Results show that SPMS reduces the disaggregation methodology error of SH and DHW compared to the truncated data. The optimal dataset outperforms the current methodology, indicating a potential for improving and scaling the methodology for larger datasets

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