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. 2024 Aug 28;11(1):938.
doi: 10.1038/s41597-024-03770-7.

A two-year dataset of energy, environment, and system operations for an ultra-low energy office building

Jung Min Han  1   2   3 , Ali Malkawi  1   2 , Xu Han  4   5   6 , Sunghwan Lim  1   2 , Elence Xinzhu Chen  1   2 , Sang Won Kang  1   2 , Yiwei Lyu  1   2 , Peter Howard  2
Affiliations

A two-year dataset of energy, environment, and system operations for an ultra-low energy office building

Jung Min Han et al. Sci Data. .

Abstract

This paper describes a two-year high-fidelity dataset for an ultra-low energy office building and living laboratory called HouseZero®. The building integrates multiple low-energy technologies, such as natural ventilation with automatic windows, ground source heat pump, and thermally activated building systems. The building's performance is continuously monitored with an extensive sensor network. The dataset consists of breakdown energy end uses, photovoltaic (PV) production, zone-level indoor environment including indoor air temperature, CO2 concentration, and relative humidity, micro-climatical conditions, building façade temperature, and detailed system operations including zone-level BTU meter, valve status, slab temperature, window/skylight opening status, heat pump, and geothermal well operations. The data can be used to support data analytics of ultra-low-energy building operations, and data-driven modeling of low-energy building systems.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The office building HouseZero® in Cambridge, Massachusetts, USA.
Fig. 2
Fig. 2
Layout of each floor and thermal zones.
Fig. 3
Fig. 3
Schematics of the building systems in HouseZero®.
Fig. 4
Fig. 4
Workflow of the data collection and processing.
Fig. 5
Fig. 5
Locations of the building façade temperature sensors.
Fig. 6
Fig. 6
An example of outlier filtering using Z-score.
Fig. 7
Fig. 7
An example of data repetition filtering.
Fig. 8
Fig. 8
Pie chart of the breakdown energy end uses for two years.
Fig. 9
Fig. 9
Daily pattern of electricity end uses in a sample summer day.
Fig. 10
Fig. 10
Daily pattern of electricity end uses in a sample winter day.
Fig. 11
Fig. 11
Daily pattern of energy rate data from a BTU meter in a sample winter day.
Fig. 12
Fig. 12
Daily pattern of the indoor CO2 concentrations in a sample summer day.
Fig. 13
Fig. 13
Daily pattern of relative humidity in a sample summer day.
Fig. 14
Fig. 14
Indoor temperature and CO2 with natural ventilation in the passive mode.
Fig. 15
Fig. 15
Monthly PV production of Year 1.
Fig. 16
Fig. 16
Operation of the TABS in a sample summer day of Zone 23.
Fig. 17
Fig. 17
Operation of the TABS in a sample winter day of Zone 23.
Fig. 18
Fig. 18
Operation of the heat pump in a sample winter day.
See this image and copyright information in PMC

References

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    1. Chen, E. X., Han, X., Malkawi, A. & Li, N. Ensembled Deep Learning-based Model Predictive Control for Automatic Window Operations in Winter. in 2023 ASHRAE Winter Conference (Atlanta, Georgia, 2023).
    1. Chen, E. X., Han, X., Malkawi, A., Zhang, R. & Li, N. Adaptive model predictive control with ensembled multi-time scale deep-learning models for smart control of natural ventilation. Build Environ, 110519 (2023).

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