SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods

Jonathan Roth, Amory Martin, Clayton Miller and Rishee K. Jain

Applied Energy, 2020, vol. 280, issue C, No S0306261920314306

Abstract: Cities officials are increasingly interested in understanding spatial and temporal energy patterns of the built environment to facilitate their city’s transition to a low-carbon future. In this paper, a new Augmented-Urban Building Energy Model (A-UBEM) is proposed that combines data-driven and physics-based simulation methods to produce synthetic hourly load curve estimates for every building within a city—similar to data an hourly smart meter would measure. By using only publicly available data, a generalizable two-step process is implemented—that other cities with similar available data can replicate—using New York City as a case study. Step (1) estimates the annual energy use for every building in the city using supervised machine learning algorithms. Step (2) extends these results and leverages physics-based simulation models through a convex optimization formulation that minimizes the squared difference between the aggregated building demand and the observed city-wide hourly electricity demand. Results from step (1) show that the Random Forest algorithm performs best with a mean log squared error of 0.293, while the convex optimization in step (2) results in a mean training error of 6.11% mean absolute percentage error (MAPE). To validate the stability of the produced load curves, Monte Carlo simulations are conducted, using random subsets of buildings from the city, which produce an out-of-sample error averaging 6.41% MAPE across each simulation. Particle swarm optimization is also explored—using the results from the Monte Carlo simulation—to assess if the model could be improved by relaxing certain constraints, but marginal error reductions are found, further proving the stability of the proposed model. Overall, A-UBEM is a first step towards creating highly granular urban-scale synthetic hourly load curves solely using open data. Such load curves are integral for planning sustainable cities and accelerating the adoption of low-carbon distributed energy resources (DERs) and district energy systems.

Keywords: Urban building energy model; Supervised machine learning; Convex optimization; Smart meter; Energy efficiency; Energy prediction (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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DOI: 10.1016/j.apenergy.2020.115981

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