Utility-scale Building Type Assignment Using Smart Meter Data
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Electricity (OE)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1820853
- Resource Relation:
- Conference: Building Simulation Conference (BuildSim 2021) - Bruges, , Belgium - 9/1/2021 12:00:00 PM-9/3/2021 12:00:00 PM
- Country of Publication:
- United States
- Language:
- English
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