Representing Small Commercial Building Faults in EnergyPlus, Part II: Model Validation

Buildings

Stephen Frank¹  JamesE. Braun¹  Janghyun Kim²  Piljae Im²  David Goldwasser³  Matt Leach⁴ 

[1] Thermal Sciences Center, 15013 Denver West Parkway, Golden, CO 80401, USA;;National Renewable Energy Laboratory, Buildings &Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA;School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA;

关键词: automated fault detection and diagnosis;    fault model;    building energy modeling;    energyplus;    openstudio;    validation;    fault experiment;   

DOI  :  10.3390/buildings9120239

来源: DOAJ

【 摘 要 】

Automated fault detection and diagnosis (AFDD) tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, access to high-quality training data for such algorithms 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 II (this paper) first presents a methodology of validating fault models with OpenStudio and then presents validation results, which are compared against measurements from a reference building. We discuss the results of our experiments with eight different faults in the reference building (a total of 39 different baseline and faulted scenarios), including our methodology for using fault models along with the reference building model to simulate the same faulted scenarios. Then, we present validation of the fault models by comparing results of simulations and experiments either quantitatively or qualitatively.

【 授权许可】

Unknown