Validation of a new method to estimate energy use for space heating and hot water production from low-resolution heat meter data

Daniel Leiria*, Hicham Johra, Evangelos Belias, Davide Quaggiotto, Angelo Zarrella, Anna Marszal-Pomianowska, Michal Zbigniew Pomianowski


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Denmark aims to be independent of fossil fuels in the country's energy production by 2050. One of the initiatives to reach the decarbonization goal is the digitalization of the energy sector, specifically the roll-out of smart meters in the buildings connected to the district heating network. As a result, it allowed having better insights into the dynamics of the heating loads of the demand side. However, these meters often record the total energy usage without distinguishing between the energy use for space heating (SH) and domestic hot water (DHW). Additionally, the metered data have hourly resolution, which prevents the detection of short DHW usage. To tackle this limitation and gain valuable information on the buildings' heating patterns, this paper presents a new methodology to estimate the energy use for SH and DHW from total measurements in residential buildings. The method employs a combined smoothing algorithm with a support vector regression to estimate the energy use for SH from outdoor conditions. The energy use for DHW is calculated a posteriori by the difference between the total measurements and the estimated SH energy. The advantage of this technique is the ability to be applied in hourly-resolution data while only requiring local weather measurements, making it a tool to be utilized in different scenarios. This method is validated with three different sets of building cases. The first dataset consists of 28 apartments in Denmark, where the measurement resolution is coarse at 1 kWh. This case focuses on determining the method's accuracy in single-family dwellings when their measurements are truncated. The second dataset set of apartments is located in a 5-story building in Switzerland. In this case, the objective is to test the method's accuracy when analyzing aggregated measurements of all dwellings in the building. The third dataset includes hourly readings from customers connected to a DH network in Italy. In this case, the objective is to test the method's application to other building typologies (i.e., historical buildings). Because these three cases are located in different countries, this validation study also tests the method's robustness to the variability of users, locations, and heating system types.
TitelBuildSim Nordic 2022 - Conference Proceedings
Antal sider8
ForlagEDP Sciences
Publikationsdato1 dec. 2022
KapitelBuildings, Districts and Energy
StatusUdgivet - 1 dec. 2022
BegivenhedBuildSim Nordic Conference 2022 - Copenhagen, Copenhagen, Danmark
Varighed: 22 aug. 202223 aug. 2022


KonferenceBuildSim Nordic Conference 2022
NavnE3S Web of Conferences


  • District Heating
  • Space Heating
  • Domestic Hot Water
  • BSN
  • Machine Learning
  • Smart Heat Meter
  • Energy demand


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