Dynamic thermal simulation models of buildings: New method for empirical validation

Lomas, Kevin J.

doi:10.1177/014362449101200101

Cited by 14 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study, see Brembilla et al (2018), validated the 1D model of hydronic floor heating (used in the current paper) developing an innovative validation methodology. The study applied the validation technique developed in Lomas (1991) (recently applied in Strachan et al (2016)) at the model predictions obtained in a booth simulator. The booth simulator reduced the amount of disturbances (from outdoor and indoor climate) by affecting the thermal output of the floor heating (heat released upwardsq up ) and the average floor surface temperature (T f ).…”

Section: Answer To Rq A) Accuracymentioning

confidence: 99%

Paradoxes in understanding the Efficiency Factors of Space Heating

et al. 2018

View full text Add to dashboard Cite

Efficiency factors are here defined as the thermal energy performance indicators of the space heating. Until recently, the efficiency factors were assumed as one value for space heating located in any climate. This study addresses the problem of how the outdoor climate affects the efficiency factors of a space heating equipped with 1D model of hydronic floor heating. The findings show how the efficiency factors, computed with two numerical methods, are correlated with the solar radiation. This study highlights the paradoxes in understanding the results of efficiency factors analysis. This work suggests how to interpret and use the efficiency factors as a benchmark performance indicator.

show abstract

Section: Answer To Rq A) Accuracymentioning

confidence: 99%

Paradoxes in understanding the Efficiency Factors of Space Heating

et al. 2018

View full text Add to dashboard Cite

show abstract

“…For rectification, the input data/parameters are changed systematically and logically within their bounds of plausibility. The results of a method that indiscriminately changes input data/parameters for validating the model have to be seen with scepticism because it can be declared as a not good modus operandi as stated by Lomas (1991). The practice of rectification of input data/parameters has named with calibration as stated in Savic et al (2009).…”

Section: Review On Hydronic Floor Heating Models and Empirical Validamentioning

confidence: 99%

Predictions’ robustness of one-dimensional model of hydronic floor heating: novel validation methodology using a thermostatic booth simulator and uncertainty analysis

Brembilla

Östin

Olofsson

2017

Journal of Building Physics

View full text Add to dashboard Cite

Hydronic floor heating models provide predictions in estimating heat transfer rates and floor surface temperature. Information on the model performance and range of validity of its results are often lacking in literature. Researchers have to know the accuracy and robustness of the model outcomes for performing energy and climate comfort calculations. This article proposes a novel validation methodology based on the uncertainty analysis of input data/parameters of one-dimensional model of hydronic floor heating tested in a thermostatic booth simulator and compared with experimental measurements. The main results are: (1) prediction accuracy between 0.4% and 2.9% for [Formula: see text] and between 0.7% and 7.8% for [Formula: see text] when the serpentine has tube spacing (p) of 0.30 m, (2) prediction accuracy between 0.5% and 1.4% for [Formula: see text] and between 8.7% and 12.9% for [Formula: see text] with p = 0.15 m and (3) [Formula: see text] mostly affects predictions with oscillations between 6.2% and 2.2% for [Formula: see text]. This model provides robust and reliable predictions exclusively for [Formula: see text] when p = 0.30 m.

show abstract

“…('8) It is a well established and tested (19) program, and has the fastest running speed of the three, Ideally a very diverse set of building types would be used for a wide range of simulations, using both SRYS and TRYS, with the differences noted. Unfortunately constraints of time and computing cost require both the Table 3 Base-case building material data number and complexity of the building types to be limited.…”

Section: The Selection Of Srys For Europementioning

confidence: 99%

Direct-gain dwellings: Accuracy of short reference years for predicting thermal performance

Waide¹,

Norton

1995

Building Services Engineering Research and Technology

View full text Add to dashboard Cite

Ten 'short reference year' weather data sets were compared with three full weather sets. Using the SERI-RES building thermal analysis program, comparative simulations were performed on range of buildings. Graphs illustrating the efficacy of the short reference years when used to predict a range of parameters are presented. Short reference years comprising five days per month provide the best composite accuracy.

show abstract

Dynamic thermal simulation models of buildings: New method for empirical validation

Cited by 14 publications

References 6 publications

Paradoxes in understanding the Efficiency Factors of Space Heating

Paradoxes in understanding the Efficiency Factors of Space Heating

Predictions’ robustness of one-dimensional model of hydronic floor heating: novel validation methodology using a thermostatic booth simulator and uncertainty analysis

Direct-gain dwellings: Accuracy of short reference years for predicting thermal performance

Contact Info

Product

Resources

About