Estimation of temperature setpoints and heat transfer coefficients among residential buildings in Denmark based on smart meter data

Panagiota Gianniou*, Christoph Reinhart, David Hsu, Alfred Heller, Carsten Rode

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 °C and 19.1 °C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia.
Original languageEnglish
JournalBuilding and Environment
Volume139
Pages (from-to)125-133
Number of pages9
ISSN0360-1323
DOIs
Publication statusPublished - 2018

Keywords

  • Housing stock model
  • Smart meter data
  • Temperature setpoints
  • Thermal comfort preferences
  • U-values
  • Urban scale

Cite this

@article{230b5150df574fd7bf275bb8b1884d0c,
title = "Estimation of temperature setpoints and heat transfer coefficients among residential buildings in Denmark based on smart meter data",
abstract = "Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 °C and 19.1 °C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia.",
keywords = "Housing stock model, Smart meter data, Temperature setpoints, Thermal comfort preferences, U-values, Urban scale",
author = "Panagiota Gianniou and Christoph Reinhart and David Hsu and Alfred Heller and Carsten Rode",
year = "2018",
doi = "10.1016/j.buildenv.2018.05.016",
language = "English",
volume = "139",
pages = "125--133",
journal = "Building and Environment",
issn = "0360-1323",
publisher = "Pergamon Press",

}

Estimation of temperature setpoints and heat transfer coefficients among residential buildings in Denmark based on smart meter data. / Gianniou, Panagiota; Reinhart, Christoph; Hsu, David; Heller, Alfred; Rode, Carsten.

In: Building and Environment, Vol. 139, 2018, p. 125-133.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Estimation of temperature setpoints and heat transfer coefficients among residential buildings in Denmark based on smart meter data

AU - Gianniou, Panagiota

AU - Reinhart, Christoph

AU - Hsu, David

AU - Heller, Alfred

AU - Rode, Carsten

PY - 2018

Y1 - 2018

N2 - Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 °C and 19.1 °C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia.

AB - Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 °C and 19.1 °C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia.

KW - Housing stock model

KW - Smart meter data

KW - Temperature setpoints

KW - Thermal comfort preferences

KW - U-values

KW - Urban scale

U2 - 10.1016/j.buildenv.2018.05.016

DO - 10.1016/j.buildenv.2018.05.016

M3 - Journal article

VL - 139

SP - 125

EP - 133

JO - Building and Environment

JF - Building and Environment

SN - 0360-1323

ER -