Calibrating whole building energy models: Detailed case study using hourly measured data

Raftery, Paul; Keane, Marcus; Costa, Andrea

doi:10.1016/j.enbuild.2011.09.039

Cited by 168 publications

(81 citation statements)

References 6 publications

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“…Graphical plots of calibration signature and characteristic signature have also been proposed to guide the parameter tuning process (Liu and Liu, 2011). Due to its iterative nature, the use of version control to keep track of model changes and the reasons for the change has been identified as an important process in order to improve the reproducibility of manual calibration methods (Raftery et al, 2011).…”

Section: Manual Calibration Approachesmentioning

confidence: 99%

“…Furthermore, since these calibration criteria are based solely on energy consumption and do not account for uncertainties and inaccuracies of the input parameters, this makes their reliability questionable. Calls have also now been made to use hourly measured data for calibration purposes for the reason that the model would better represent the building's actual performance, since calibration with monthly data could easily miss significant errors at a daily or hourly resolution (Reddy, 2006;Raftery et al, 2011).…”

Section: Assessing Calibration Performancementioning

confidence: 99%

See 1 more Smart Citation

Bayesian calibration of building energy models with large datasets

Chong

Lam

Pozzi

et al. 2017

Energy and Buildings

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Section: Manual Calibration Approachesmentioning

confidence: 99%

Section: Assessing Calibration Performancementioning

confidence: 99%

Bayesian calibration of building energy models with large datasets

Chong

Lam

Pozzi

et al. 2017

Energy and Buildings

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“…Designers of integrated control systems for existing buildings usually develop a model relating to the current scenario of the buildings as a first step; then, a calibration methodology is applied to assure accuracy; finally, several opportunities for energy saving supplied by renovation actions and enhanced control systems are assessed based on the same calibrated model (Raftery et al (2011)). In conclusion, whenever enhanced control systems integrated in existing large buildings must be simulated, the use of accurate dynamic simulation tools is mandatory.…”

Section: Simulation Of Integrated Systemsmentioning

confidence: 99%

“…Error (CVRMSE) values were calculated using the formulae suggested by Committee et al (2002), which have been already applied both for the validation of energy models against hourly measurements (Raftery et al (2011)) and for the calibration of transient models based on Modelica/Dymola (Ali et al (2015)):…”

Section: Mean Bias Error (Mbe) and Cumulative Variation Of Root Mean mentioning

confidence: 99%

Development and calibration of a model for the dynamic simulation of fans with induction motors

Vaccarini

Carbonari

Casals

2017

Applied Thermal Engineering

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In this paper a model for the dynamic simulation of fans used in mechanical air supply systems is described. Thanks to this model, the behavior of fans subject to control by variable frequency drives (VFD) can be predicted, which includes power absorbed by the fan and expected ventilation rates. Hence, it can help design energy control systems for buildings. The proposed model was based on the Modelica language and was developed from the dynamic phasor domain representation, because this representation is a trade-off between the basic non transient representation, that is computationally efficient but cannot describe fan dynamics, and the dynamic time domain model, that is the most representative one but computationally very demanding. A comparison among these models showed that, within fan frequency variations typical of ventilation systems in buildings, the phasor domain model is as representative as the more complex dynamic time domain model in terms of prediction of the dynamic behavior, that is neglected by the basic non transient model. Moreover, the new phasor domain model was validated against measured data relative to a fan installed in a subway station in Barcelona. Thanks to this model, energy consumption of dynamically driven fans can be estimated at the simulation stage, at the expense of a reasonable computational effort.Peer ReviewedPostprint (author's final draft

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“…However, these methods fail when they involve building-to-building comparison when all the compared buildings operate inefficiently and when the methods do not comprehensively take into account each building's unique nature. In the cases where a calibrated wholebuilding energy simulation model exists, measured performance is extremely difficult to compare with predicted performance [9][10][11]. To complicate matters further, measured HVAC time-series data describe building performance in higher resolution but wholly depend upon weather and control strategies [12].…”

Section: Introductionmentioning

confidence: 99%

Scenario modelling: A holistic environmental and energy management method for building operation optimisation

O’Donnell

Keane

Morrissey

et al. 2013

Energy and Buildings

Self Cite

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Building managers have specific duties and certain outputs that are required of them. Without the necessary data, information, tools, and time, they are unable to adequately meet their organisational goals. Scenario modelling enables explicit and unambiguous coupling of building functions with other pivotal aspects of building operation in a method that specifically considers the education and technical expertise of building managers. This new method captures, transforms, and communicates the complex interdependencies of environmental and energy management in buildings through an easily navigable, holistic, and reproducible checking mechanism that compares actual performance with predicted performance and completes the "plan-docheck-act" cycle for building managers. Most important, the structured nature of this method caters to the diverse profile of building managers, making it applicable for widespread deployment. This paper demonstrates the benefit of using the new method by examining its application to a performance analysis of two existing buildings.

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Calibrating whole building energy models: Detailed case study using hourly measured data

Cited by 168 publications

References 6 publications

Bayesian calibration of building energy models with large datasets

Bayesian calibration of building energy models with large datasets

Development and calibration of a model for the dynamic simulation of fans with induction motors

Scenario modelling: A holistic environmental and energy management method for building operation optimisation

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