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Title Authors PubMed ID
1 Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate Sartori I; Walnum HT; Skeie KS; Georges L; Knudsen MD; Bacher P; Candanedo J; Sigounis AM; Prakash AK; Pritoni M; Granderson J; Yang S; Wan MP; 37153123
ENCS

 

Title:Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate
Authors:Sartori IWalnum HTSkeie KSGeorges LKnudsen MDBacher PCandanedo JSigounis AMPrakash AKPritoni MGranderson JYang SWan MP
Link:https://pubmed.ncbi.nlm.nih.gov/37153123/
DOI:10.1016/j.dib.2023.109149
Publication:Data in brief
Keywords:CSV filesDeep Reinforcement Learning (DRL)HeatingHigh resolutionModel Predictive Control (MPC)Model identificationPseudo-Random Binary Sequence (PRBS)Ventilation and Air Conditioning (HVAC)
PMID:37153123 Category: Date Added:2023-05-08
Dept Affiliation: ENCS

Description:

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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