Empirical modeling of the impacts of faults on water-cooled chiller power consumption for use in building simulation programs

Cheung, Howard; Braun, James E.

doi:10.1016/j.applthermaleng.2016.01.119

Cited by 32 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, refrigerant undercharge in a vapor compression system can be simulated by modifying existing parameters in the building simulation, such as COP and capacity, which have a nonlinear dependence on charge. In these cases, empirical or semiempirical models [27][28][29][30][31] derived from measurements or detailed physical models can be used.…”

Section: Prior Workmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Prior Workmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cheung and Braun developed the fault models for a variety of typical building energy system equipment with three modeling techniques: empirical modeling, semi-empirical modeling, and physical modeling [4,16]. Radhakrishnana, et al…”

Section: A Variety Of Fault Detection and Diagnosis (Fdd) Tools Have mentioning

confidence: 99%

Modeling of HVAC operational faults in building performance simulation

2017

View full text Add to dashboard Cite

Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level-an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. The results demonstrate that the faults have significant impacts on building energy performance as well as on occupant thermal comfort. Finally, the paper introduces future development plans for EnergyPlus fault-modeling capability.

show abstract

“…Several previous studies focused on capturing the impact of a fault in the building component with experimental measurements. These are either full experimental studies to measure the performance degradation caused by the fault [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] or studies focusing on developing fault models or FDD algorithms that use experimental measurements as a basis for development and validation [29][30][31][32][33][34][35][36][37][38][39][40]. These studies cover various faults that occur in buildings at the component level:…”

Section: Introductionmentioning

confidence: 99%

“…Although these studies provide useful information related to the individual objectives of each study, only one study focused on fault models that can be directly used in building energy simulations tools such as EnergyPlus ® and OpenStudio ® . Cheung and Braun [31] developed empirical models of faults (overcharging, excess oil, noncondensable, and condenser fouling) that are common in water-cooled centrifugal chillers. Because chillers are not common in small commercial buildings, however, chiller fault models are not considered in the scope of this study.…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Frank

et al. 2019

Buildings

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Empirical modeling of the impacts of faults on water-cooled chiller power consumption for use in building simulation programs

Cited by 32 publications

References 14 publications

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

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

Modeling of HVAC operational faults in building performance simulation

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

Contact Info

Product

Resources

About