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Open AccessArticle

Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development

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National Renewable Energy Laboratory, Buildings & Thermal Sciences Center, 15013 Denver West Parkway, Golden, CO 80401, USA
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School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA
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Ray W. Herrick Laboratories, Purdue University, 140 S. Martin Jischke Dr., West Lafayette, IN 47907, USA
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Author to whom correspondence should be addressed.
Buildings 2019, 9(11), 233; https://doi.org/10.3390/buildings9110233
Received: 5 October 2019 / Revised: 6 November 2019 / Accepted: 8 November 2019 / Published: 14 November 2019
Small commercial buildings (those with less than approximately 1000 m2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building. View Full-Text
Keywords: automated fault detection and diagnosis; data-driven AFDD; fault model; building energy modeling; EnergyPlus; OpenStudio automated fault detection and diagnosis; data-driven AFDD; fault model; building energy modeling; EnergyPlus; OpenStudio
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    Link: https://openei.org/doe-opendata/dataset/curated-modeled-fault-data-set
    Description: The curated fault model simulation data set consists of tagged and fully-described time series representing simulated faults for the AFDD test building (ORNL’s Flexible Research Platform (FRP)), including baseline performance, faulty performance, and corresponding energy impact. A total of 26 fault models are considered for 99 simulation scenarios with various fault intensity levels.
MDPI and ACS Style

Kim, J.; Frank, S.; Braun, J.E.; Goldwasser, D. Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development. Buildings 2019, 9, 233.

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