Abstract
United States building energy use accounted for 40% of total energy use, 74% of peak demand, and $412 billion in 2019. Building energy modeling allows researchers to simulate building physics, gain insights into possible energy/demand saving opportunities, and assess cost-effective resilience amidst climate change. Many building features needed to create building energy models are readily available such as 2D footprints and LiDAR (height). A critical feature that is not generally obtainable is the building type. In partnership with a utility, a years worth of real-world, 15-minute electrical use data has been examined. The smart meter data is compared to 97 different prototype building energy models to assign building type. Real-world considerations including data preparation, quality assurance, and handling of missing values for advanced metering infrastructure data are addressed. Euclidean distance for pattern-matching of energy use, dynamic time warping, and time-window statistics with machine learning are compared for determining building type from measured electricity use.
| Original language | English |
|---|---|
| Title of host publication | BS 2021 - Proceedings of Building Simulation 2021 |
| Subtitle of host publication | 17th Conference of IBPSA |
| Editors | Dirk Saelens, Jelle Laverge, Wim Boydens, Lieve Helsen |
| Publisher | International Building Performance Simulation Association |
| Pages | 3196-3205 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781775052029 |
| DOIs | |
| State | Published - 2022 |
| Event | 17th IBPSA Conference on Building Simulation, BS 2021 - Bruges, Belgium Duration: Sep 1 2021 → Sep 3 2021 |
Publication series
| Name | Building Simulation Conference Proceedings |
|---|---|
| ISSN (Print) | 2522-2708 |
Conference
| Conference | 17th IBPSA Conference on Building Simulation, BS 2021 |
|---|---|
| Country/Territory | Belgium |
| City | Bruges |
| Period | 09/1/21 → 09/3/21 |
Bibliographical note
Publisher Copyright:© International Building Performance Simulation Association, 2022
Funding
This work was funded by field work proposal CEBT105 under US Department of Energy Building Technology Office Activity Number BT0305000, as well as Office of Electricity Activity Number TE1103000. The authors would like to thank Amir Roth and Madeline Salzman for their support and review of this project. The authors would also like to thank Mark Adams for his contributions to the AutoBEM software for model generation, and Jibonananda Sanyal for AutoSIM contributions for scalable simulation. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work was funded by field work proposal CEBT105 under US Department of Energy Building Technology Office Activity Number BT0305000, as well as Office of Electricity Activity Number TE1103000. The authors would like to thank Amir Roth and Madeline Salzman for their support and review of this project. The authors would also like to thank Mark Adams for his contributions to the AutoBEM software for model generation, and Jibo-nananda Sanyal for AutoSIM contributions for scalable simulation.
| Funders | Funder number |
|---|---|
| DOE Public Access Plan | |
| US Department of Energy Building Technology | BT0305000, DE-AC05-00OR22725, TE1103000 |
| United States Government | |
| U.S. Department of Energy |
