1. Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development
- Author
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David Goldwasser, James E. Braun, Janghyun Kim, and Stephen Frank
- Subjects
Structure (mathematical logic) ,Training set ,data-driven AFDD ,Computer science ,automated fault detection and diagnosis ,0211 other engineering and technologies ,Building energy ,02 engineering and technology ,Building and Construction ,Fault (power engineering) ,Fault detection and isolation ,Reliability engineering ,Set (abstract data type) ,fault model ,EnergyPlus ,021105 building & construction ,Architecture ,Model development ,021108 energy ,Fault model ,OpenStudio ,building energy modeling ,Civil and Structural Engineering - Abstract
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.
- Published
- 2019
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