Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.apenergy.2020.115981
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dc.titleSynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods
dc.contributor.authorRoth, Jonathan
dc.contributor.authorMartin, Amory
dc.contributor.authorMiller, Clayton
dc.contributor.authorJain, Rishee K
dc.date.accessioned2021-04-15T03:23:18Z
dc.date.available2021-04-15T03:23:18Z
dc.date.issued2020-12-15
dc.identifier.citationRoth, Jonathan, Martin, Amory, Miller, Clayton, Jain, Rishee K (2020-12-15). SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods. APPLIED ENERGY 280. ScholarBank@NUS Repository. https://doi.org/10.1016/j.apenergy.2020.115981
dc.identifier.issn03062619
dc.identifier.issn18729118
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/189342
dc.description.abstractCities 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.
dc.language.isoen
dc.publisherELSEVIER SCI LTD
dc.sourceElements
dc.subjectUrban building energy model
dc.subjectSupervised machine learning
dc.subjectConvex optimization
dc.subjectSmart meter
dc.subjectEnergy efficiency
dc.subjectEnergy prediction
dc.typeArticle
dc.date.updated2021-04-15T02:22:49Z
dc.contributor.departmentDEPT OF BUILDING
dc.description.doi10.1016/j.apenergy.2020.115981
dc.description.sourcetitleAPPLIED ENERGY
dc.description.volume280
dc.published.statePublished
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