AlphaBuilding - Synthetic Buildings Operation Dataset

Li, Han; Wang, Zhe; Hong, Tianzhen

doi:10.25984/1784722

Title: AlphaBuilding - Synthetic Buildings Operation Dataset

Dataset
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Abstract

This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

Authors:

;

Lawrence Berkeley National Laboratory

Publication Date:: Sun Dec 20 23:00:00 EST 2020

Other Number(s):: 2977

Research Org.:: DOE Open Energy Data Initiative (OEDI); Lawrence Berkeley National Laboratory

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)

Collaborations:: LBNL

Subject:: AlphaBuilding; Array; HVAC; MEL; Synthetic; building; efficiency; energy; energy consumption; environmental; lighting; miscellaneous electric loads; occupancy; simulation; synthetic data

OSTI Identifier:: 1784722

DOI:: https://doi.org/10.25984/1784722

Citation Formats


                    Li, Han, Wang, Zhe, and Hong, Tianzhen. AlphaBuilding - Synthetic Buildings Operation Dataset.  United States: N. p., 2020. 
        Web.  doi:10.25984/1784722.

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                    Li, Han, Wang, Zhe, & Hong, Tianzhen. AlphaBuilding - Synthetic Buildings Operation Dataset.  United States.  doi:https://doi.org/10.25984/1784722

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                    Li, Han, Wang, Zhe, and Hong, Tianzhen. 2020.  
"AlphaBuilding - Synthetic Buildings Operation Dataset".  United States.  doi:https://doi.org/10.25984/1784722.  https://www.osti.gov/servlets/purl/1784722. Pub date:Sun Dec 20 23:00:00 EST 2020

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@article{osti_1784722,

  title        = {AlphaBuilding - Synthetic Buildings Operation Dataset},

  author       = {Li, Han and Wang, Zhe and Hong, Tianzhen},

  abstractNote = {This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.},

  doi          = {10.25984/1784722},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Dec 20 23:00:00 EST 2020},

  month        = {Sun Dec 20 23:00:00 EST 2020}

}

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DOI: https://doi.org/10.25984/1784722

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