Abstract
Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.
| Original language | English |
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| Title of host publication | 2023 International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023 |
| Editors | Mohammad Alsmirat, Yaser Jararweh, Moayad Aloqaily, Jaime Lloret |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 91-98 |
| Number of pages | 8 |
| ISBN (Electronic) | 9798350339253 |
| DOIs | |
| State | Published - 2023 |
| Event | 3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023 - Kuwait City, Kuwait Duration: Oct 24 2023 → Oct 26 2023 |
Publication series
| Name | 2023 International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023 |
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Conference
| Conference | 3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023 |
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| Country/Territory | Kuwait |
| City | Kuwait City |
| Period | 10/24/23 → 10/26/23 |
Funding
Notice of Copyright. 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 for his support and review of this project. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC, under Contract Number DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE 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).
Keywords
- Bias Correction
- Machine Learning
- Quantile Mapping
- Random Forest
- Urban-scale building energy modeling