Confidence interval computation method for dynamic performance evaluations of solar thermal collectors

Zirkel-Hofer, Annie; Perry, Stephen D.; Krämer, Korbinian; Heimsath, Anna; Scholl, Stephan; Platzer, Werner

doi:10.1016/j.solener.2018.01.041

Cited by 6 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The residuals of randomly selected measurement sequences from the original data are used to generate a large number of new data sets, which can then be parameterized again. This process is repeated many times and thus a large number of different parameter sets for the collector are generated, whose distribution and co-variances can then be analyzed and used to assess the quality of the parameters [11]. Significant dependencies between different parameters as well as distributions of individual parameters, which deviate strongly from a normal distribution, can indicate an insufficient database.…”

Section: Resultsmentioning

confidence: 99%

Review of In Situ Test Methods for Solar Collectors and Solar Collector Arrays

Fahr¹,

Tschopp²,

Nielsen³

et al. 2020

Self Cite

View full text Add to dashboard Cite

This fact sheet presents three in situ test methods for solar collectors and solar collector arrays, namely In situ Collector Certification (ICC), Performance Check for Collector Arrays (PC) and Dynamic Collector Array Test (D-CAT). A comparison is made regarding their scopes and use cases, methodologies and outcomes, which could serve as a decision-making aid for stakeholders in selecting the procedure that best suits their needs. The analysis shows that the methods do not contradict, but rather complement each other.

show abstract

Section: Resultsmentioning

confidence: 99%

Review of In Situ Test Methods for Solar Collectors and Solar Collector Arrays

Fahr¹,

Tschopp²,

Nielsen³

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The residuals of randomly selected measurement sequences from the original data are used to generate a large number of new data sets, which can then be parameterized again. This process is repeated many times and thus a large number of different parameter sets for the collector are generated, whose distribution and co-variances can then be analyzed and used to assess the quality of the parameters (Zirkel-Hofer et al, 2018). Significant dependencies between different parameters as well as distributions of individual parameters, which deviate strongly from a normal distribution, can indicate an insufficient database.…”

Section: Resultsmentioning

confidence: 99%

Review and Outlook on Methods for Product Certification, Energy Yield Measurement, Power Output Determination and Commissioning Test for Large Solar Thermal Installations

Krämer¹,

Tschopp²,

Nielsen³

et al. 2019

Proceedings of the ISES Solar World Congress 2019

View full text Add to dashboard Cite

The market for large solar thermal systems is growing and with it the need to plan, design and simulate these systems precisely. This requires the ability to accurately and meaningfully measure their performance and yields. Hence activities for the development of methods to characterize such plants are on-going. The paper presents three of these methods and compares them with regard to their technical requirements and boundary conditions. The comparison includes the collector models on which they are based, the nature of their results and the use cases for which they are most suitable. It should serve as a decision-making aid for stakeholders in selecting the measurement method best suited to their needs and boundaries. The paper shows that these methods do not contradict, but rather complement each other.

show abstract

“…Figure 5 presents a confidence interval calculation based on a Bootstrapping approach [9] for the ParaID approach. Bootstrapping is a technique, where artificial data sets -obtained from resampling of the original measurement data -allow generating a probability distribution for the identification results.…”

Section: Confidence Interval Calculation For Paraid Approach With Boo...mentioning

confidence: 99%

Comparison of advanced parameter identification methods for linear Fresnel collectors in application to measurement data

Schöttl

Montenon

Papanicolas

et al. 2022

AIP Conference Proceedings

Self Cite

View full text Add to dashboard Cite

Modeling of the Linear Fresnel collectors in a real environment using standardized, state-of-the-art approaches is still inaccurate, yet it is crucial for robust loop control and collector yield assessment. Two advanced parameter identification methodologies -ParaID by Fraunhofer ISE and RealTrackEff by the Cyprus Institute -are compared in terms of the underlying equations, which extend ISO 9806. Both approaches are then applied to a Linear Fresnel collector research facility at the Cyprus Institute, for which a database of more than 50 measurement days is available, including reflectometric measurements. For both methods, several variations with increasing complexity are tested and the quality of the resulting fit in terms of outlet temperature is studied. Both methods take into account soiling/cleanliness and nonlinear collector behavior. While ParaID focuses on the identification of the IAMs in a real environment, the method by the Cyprus Institute links the efficiency to non-linear tracking effects. Real collector characteristics like varying cleanliness and asymmetric collector behavior are found to have a strong impact on the collector performance and the identified parameters. For the ParaID approach, additional results regarding confidence intervals based on Bootstrapping and regarding identified Incidence Angle Modifiers are presented.

show abstract

Confidence interval computation method for dynamic performance evaluations of solar thermal collectors

Cited by 6 publications

References 8 publications

Review of In Situ Test Methods for Solar Collectors and Solar Collector Arrays

Review of In Situ Test Methods for Solar Collectors and Solar Collector Arrays

Review and Outlook on Methods for Product Certification, Energy Yield Measurement, Power Output Determination and Commissioning Test for Large Solar Thermal Installations

Comparison of advanced parameter identification methods for linear Fresnel collectors in application to measurement data

Contact Info

Product

Resources

About