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LBNL Fault Detection and Diagnostics Datasets

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equip...
Granderson, J. et al Lawrence Berkeley National Laboratory
Aug 01, 2022
5 Resources
0 Stars
Publicly accessible

AlphaBuilding Synthetic Buildings Operation Dataset

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...
Li, H. et al Lawrence Berkeley National Laboratory
Dec 21, 2020
5 Resources
0 Stars
Publicly accessible

Conway St Apartments Olive Street Development

In Greenfield, Massachusetts, Olive Street Development transformed an old elementary school building into 12 high-end rental apartments. The developer aimed to get as close to net-zero performance as practical, installing: (1) R-30 high-density spray foam against the brick walls (...
Aldrich, R. and Williamson, J. Steven Winter Associates of the Consortium for Advanced Residential Buildings
Apr 27, 2016
2 Resources
0 Stars
In curation

OEDI Data Management and Best Practices for Submitters and Curators

Resources for OEDI data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of OEDI. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developer...
Weers, J. et al National Renewable Energy Laboratory
Sep 01, 2022
3 Resources
1 Stars
Publicly accessible

BUTTER Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...
Tripp, C. et al National Renewable Energy Laboratory
May 20, 2022
4 Resources
0 Stars
Publicly accessible

BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...
Tripp, C. et al National Renewable Energy Laboratory
Dec 30, 2022
9 Resources
1 Stars
Publicly accessible

Analysis of Joist Masonry Moisture Content Monitoring

BSC TO5 Task 7.2 Analysis of Joist Masonry Moisture Content Monitoring House Lawrence, MA 01840 This work involved the field monitoring of embedded wood joist ends in a solid brick building in zone 5A that was retrofitted with interior insulation. Eleven joists throughout the bui...
Ueno, K. Building Science Corporation
Apr 27, 2016
3 Resources
0 Stars
Publicly accessible

Measure Guideline: Deep Energy Enclosure Retrofit for Interior Insulation of Masonry Walls and Flat Roofs

Measure Guideline: Deep Energy Enclosure Retrofit for Zero Energy Ready House Flat Roofs BSC TO5 Task 7.4 Deep Energy Enclosure Retrofit (DEER) for Interior Insulation of Masonry Walls Lawrence, MA 01840 This Measure Guideline describes a deep energy enclosure retrofit solution ...
Musunuru, S. et al Building Science Corporation
Apr 27, 2016
5 Resources
0 Stars
Publicly accessible

Evaluation of Crawlspace Retrofits in Multifamily Buildings

In 2011 and early 2012, Building Science Corporation (BSC) collaborated with Innova Services Corporation on a multifamily community un-vented crawlspace retrofit project at Oakwood Gardens in Lansdale, Pennsylvania. BSC provided design consulting services and pre and post-retrofit...
Rudd, A. Advanced Building Systems
Apr 27, 2016
41 Resources
0 Stars
Publicly accessible