Abstract
The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings.
BuildingsBench is a platform for:
- Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database.
- Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF.
We provide an index-based
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- Developers:
-
Emami, Patrick [1] ; Graf, Peter [1]
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Release Date:
- 2023-06-01
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Jupyter Notebook
Python
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 107854
- Site Accession Number:
- NREL SWR-23-51
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Emami, Patrick, and Graf, Peter.
BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51].
Computer Software.
https://github.com/NREL/BuildingsBench.
USDOE Laboratory Directed Research and Development (LDRD) Program.
01 Jun. 2023.
Web.
doi:10.11578/dc.20230828.3.
Emami, Patrick, & Graf, Peter.
(2023, June 01).
BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51].
[Computer software].
https://github.com/NREL/BuildingsBench.
https://doi.org/10.11578/dc.20230828.3.
Emami, Patrick, and Graf, Peter.
"BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]." Computer software.
June 01, 2023.
https://github.com/NREL/BuildingsBench.
https://doi.org/10.11578/dc.20230828.3.
@misc{
doecode_107854,
title = {BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]},
author = {Emami, Patrick and Graf, Peter},
abstractNote = {The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings.
BuildingsBench is a platform for:
- Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database.
- Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF.
We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.},
doi = {10.11578/dc.20230828.3},
url = {https://doi.org/10.11578/dc.20230828.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230828.3}},
year = {2023},
month = {jun}
}