AlphaBuilding-SyntheticDataset GitHub repository, which includes source code to reproduce the AlphaBuilding dataset, a Python script with an example of extracting the raw CSV outputs and saving them in a structured way, and a notebook that explores the synthetic building operation dataset generated from simulations.
Link to the AlphaBuilding synthetic dataset in the OEDI Data Lake
This notebook explores the synthetic building operation dataset generated from simulations with U.S. DOE reference detailed medium-sized office building. We demonstrate the basic data extraction and visualization, as well as example use cases of the data.
The page contains a brief introduction and Jupyter notebook to show data extraction and visualizations.
AlphaBuilding-SyntheticDataset GitHub Page
Data released under the Department of Energy's Open Energy Data Initiative (DOE). The Open Energy Data Initiative (OEDI) aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s (DOE’s) programs, offices, and national laboratories. OEDI aims to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation.
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.