Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate

Igor Sartori*, Harald Taxt Walnum, Kristian S. Skeie, Laurent Georges, Michael D. Knudsen, Peder Bacher, José Candanedo, Anna Maria Sigounis, Anand Krishnan Prakash, Marco Pritoni, Jessica Granderson, Shiyu Yang, Man Pun Wan

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

Original languageEnglish
Article number109149
JournalData in Brief
Volume48
ISSN2352-3409
DOIs
Publication statusPublished - Jun 2023

Keywords

  • CSV files
  • Deep Reinforcement Learning (DRL)
  • Heating
  • High resolution
  • Model identification
  • Model Predictive Control (MPC)
  • Pseudo-Random Binary Sequence (PRBS)
  • Ventilation and Air Conditioning (HVAC)

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