Scalable tuning of building models to hourly data

Aaron Garrett and Joshua New

Energy, 2015, vol. 84, issue C, 493-502

Abstract: Energy models of existing buildings are unreliable unless calibrated so that they correlate well with actual energy usage. Manual tuning requires a skilled professional and is prohibitively expensive for small projects, imperfect, non-repeatable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and sensors are making available. A scalable, automated methodology is needed to quickly, intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The “Autotune” project is a novel, model-agnostic methodology that leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Accuracy metrics are provided that quantify model error to measured data for either monthly or hourly electrical usage from a highly instrumented, emulated-occupancy research home.

Keywords: Autotune; EnergyPlus; Calibration; Optimization; Evolutionary computation (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:84:y:2015:i:c:p:493-502

DOI: 10.1016/j.energy.2015.03.014

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