GitHub Repository
type: Distribution
access URL: https://github.com/LBNL-ETA/AlphaBuilding-SyntheticDataset
media type: text/html
description: 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.
format: HTML
issued: 2021-06-24T00:06:07.522219
modified: 2025-11-21T12:09:47.011527

US Migrating
type: Agent

Synthetic Building Operation Dataset
type: Distribution
access URL: https://oedi-data-lake.s3-us-west-2.amazonaws.com/building_synthetic_dataset/A_Synthetic_Building_Operation_Dataset.h5
media type: application/octet-stream
description: Link to the AlphaBuilding synthetic dataset in the OEDI Data Lake
format: h5
issued: 2021-06-24T00:06:07.522229
modified: 2025-11-21T12:09:47.012324

Data Exploration Jupyter Notebook
type: Distribution
access URL: https://github.com/LBNL-ETA/AlphaBuilding-SyntheticDataset/blob/master/A%20Synthetic%20Operation%20Dataset.ipynb
media type: application/octet-stream
description: 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.
format: ipynb
issued: 2021-06-24T00:06:07.522234
modified: 2025-11-21T12:09:47.012736

AlphaBuilding-SyntheticDataset GitHub Page
type: Distribution
access URL: https://lbnl-eta.github.io/AlphaBuilding-SyntheticDataset/
media type: text/html
description: The page contains a brief introduction and Jupyter notebook to show data extraction and visualizations.
format: HTML
issued: 2021-06-24T00:06:07.522208
modified: 2025-11-21T12:09:47.011109

Dataset OEDI Registry Page
type: Distribution
access URL: https://registry.opendata.aws/oedi-data-lake/
media type: text/html
description: 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.
format: HTML
issued: 2021-06-24T00:06:07.522224
modified: 2025-11-21T12:09:47.011936

AlphaBuilding - Synthetic Dataset
type: Dataset
contact point: Organization
  fn: Han Li
keyword: north-america
keyword: geoss
keyword: united-states
keyword: amerigeoss
keyword: environmental
keyword: synthetic
keyword: energy-consumption
keyword: amerigeo
keyword: miscellaneous-electric-loads
keyword: mel
keyword: ckan
keyword: simulation
keyword: building
keyword: occupancy
keyword: efficiency
keyword: lighting
keyword: energy
keyword: hvac
keyword: synthetic-data
keyword: alphabuilding
keyword: geo
keyword: national
description: 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.
issued: 2020-12-21T07:00:00+00:00
modified: 2021-05-26T17:55:06+00:00
spatial: Location