Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA

     

Utility-scale Building Type Assignment Using Smart Meter Data

Brett Czech Bass 1, Joshua Ryan New 2, Evan Ezell 1, Eric Garrison 1, Piljae Im 2, William Copeland 3
1 University of Tennessee, Knoxville, TN United States of America
2 Oak Ridge National Laboratory, Oak Ridge, TN United States of America
3 Electric Power Board, Chattanooga, TN, United States


DOI: https://doi.org/10.26868/25222708.2021.30655
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.
Keywords: Building Energy Modeling, Urban Scale, Machine Learning, Artificial Intelligence
Pages: 3196 - 3205
Paper:
bs2021_30655