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Research Article

ROBOD, room-level occupancy and building operation dataset

Zeynep Duygu Tekler, Eikichi Ono, Yuzhen Peng, Sicheng Zhan, Bertrand Lasternas, Adrian Chong( )
Department of the Built Environment, National University of Singapore, 4 Architecture Drive, 117566, Singapore
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Abstract

The availability of the building's operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches. This paper describes a comprehensive dataset consisting of indoor environmental conditions, Wi-Fi connected devices, energy consumption of end uses (i.e., HVAC, lighting, plug loads and fans), HVAC operations, and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment. The five rooms include two different-sized lecture rooms, an office space for administrative staff, an office space for researchers, and a library space accessible to all students. A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes. This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling, building simulation and control, energy forecasting and various building analytics.

Keywords

sensor fusionoccupancy predictionbuilding operation dataoccupancy data

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Building Simulation
Volume 15 Issue 12,
December 2022
Pages 2127-2137
DOI: 10.1007/s12273-022-0925-9
Cite this article:
Tekler ZD, Ono E, Peng Y, et al. ROBOD, room-level occupancy and building operation dataset. Building Simulation, 2022, 15(12): 2127-2137. https://doi.org/10.1007/s12273-022-0925-9

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Received: 11 May 2022
Revised: 12 July 2022
Accepted: 30 July 2022
Published: 15 August 2022
© Tsinghua University Press 2022
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