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

doi:10.1016/j.dib.2023.109149

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

Department of Civil and Architectural Engineering - Building Science

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

9 Citations (Scopus)

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 language	English
Article number	109149
Journal	Data in Brief
Volume	48
ISSN	2352-3409
DOIs	https://doi.org/10.1016/j.dib.2023.109149
Publication status	Published - 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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10.1016/j.dib.2023.109149Licence: CC BY

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title = "Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate",

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.",

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doi = "10.1016/j.dib.2023.109149",

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volume = "48",

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AU - Knudsen, Michael D.

AU - Bacher, Peder

AU - Candanedo, José

AU - Sigounis, Anna Maria

AU - Prakash, Anand Krishnan

AU - Pritoni, Marco

AU - Granderson, Jessica

AU - Yang, Shiyu

AU - Wan, Man Pun

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AB - 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.

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KW - High resolution

KW - Model identification

KW - Model Predictive Control (MPC)

KW - Pseudo-Random Binary Sequence (PRBS)

KW - Ventilation and Air Conditioning (HVAC)

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

Abstract

Keywords

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