Corrections for rotating shadowband pyranometers for solar resource assessment

Geuder, Norbert; Pulvermüller, Benedikt; Vorbrugg, Oliver

doi:10.1117/12.797472

Cited by 20 publications

(28 citation statements)

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“…In all cases, the Si pyranometer of the RSI is a LICOR LI-200SA. Whereas temperature correction is similar in all versions (King and Myers 1997;Geuder, Pulvermüller, and Vorbrügg 2008), the methods for the spectral and cosine corrections vary among the publications.…”

Section: Correction Functions and Calibration Procedures For Rsismentioning

confidence: 99%

“…The combination of the GHI correction presented in Augustyn et al (2002) combined with the diffuse correction from Vignola (2006) provides a complete set of corrections for LICOR LI-200SA-based RSIs. Independently, a method for DNI, GHI, and DHI correction was developed by the DLR using functional corrections, including a particular spectral parameter composed from GHI, DHI, and DNI (Geuder, Pulvermüller, and Vorbrügg 2008). Additional corrections in dependence on AM and incidence angle were used.…”

Section: Correction Functions and Calibration Procedures For Rsismentioning

confidence: 99%

“…Becaues of the pyranometer's spectral response, sensitivity for DHI, GHI, and DNI is not the same; hence, the application of two or three calibration constants is physically reasonable even though only one sensor is used. Examples for the drift of GHI calibration constants from Geuder, Pulvermüller, and Vorbrügg (2008) were investigated for nine sensors in Geuder et al (2010). For recalibration periods between 2 years and 3¾ years, changes of this calibration constant were below 1% in most cases.…”

Section: Correction Functions and Calibration Procedures For Rsismentioning

confidence: 99%

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Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

151

121

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ForewordThe first version of this handbook was developed in response to a growing need by the solar energy industry for a single document addressing the key aspects of solar resource characterization. The solar energy industry has developed rapidly throughout the last few years, and there have been significant enhancements in the body of knowledge in the areas of solar resource assessment and forecasting. Thus, this second version of the handbook was developed from the need to update and enhance the initial version and present the state of the art in a condensed form for all of its users.Although the first version of this handbook was developed by only researchers from the National Renewable Energy Laboratory, this version has additional contributions from an international group of experts primarily from the knowledge that has been gained through participation in the International Energy Agency's Solar Heating and Cooling Programme Task 36 and Task 46.As in the first version, this material was assembled by scientists and engineers who have many decades of combined experience in atmospheric science, radiometry, meteorological data processing, and renewable energy technology development.Readers are encouraged to provide feedback to the authors for future revisions and an expansion of the handbook's scope and content. iv This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. PrefaceAs the world looks for low-carbon sources of energy, solar power stands out as the single most abundant energy resource on Earth. Harnessing this energy is the challenge for this century. Photovoltaics, solar heating and cooling, and concentrating solar power (CSP) are primary forms of energy applications using sunlight. These solar energy systems use different technologies, collect different fractions of the solar resource, and have different siting requirements and production capabilities. Reliable information about the solar resource is required for every solar energy application. This holds true for small installations on a rooftop as well as for large solar power plants. However, solar resource information is of particular interest for large installations, because they require a substantial investment, sometimes exceeding $1 billion in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need to have reliable data about the solar resource available at specific locations, including historic trends with seasonal, daily, hourly, and (preferably) subhourly variability to predict the daily and annual performance of a proposed power plant. Without this data, an accurate financial analysis is not possible.In September 2008, the U.S. Department of Energy (DOE) hosted a meeting of prominent CSP developers and stakeholders. One purpose was to identify areas in which the DOE's CSP program should focus its efforts to help the industry develop an...

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Section: Correction Functions and Calibration Procedures For Rsismentioning

confidence: 99%

Section: Correction Functions and Calibration Procedures For Rsismentioning

confidence: 99%

Section: Correction Functions and Calibration Procedures For Rsismentioning

confidence: 99%

See 1 more Smart Citation

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

151

121

View full text Add to dashboard Cite

show abstract

“…However, the difference in responsivity for GHI and DHI is specific to each individual sensor as deviations of the spectral response between different instruments occur. Thus, better results are achieved by using separate calibration factors (Geuder et al, 2008). Because DNI is the desired measurand, CFG is optimized for determination of the DNI.…”

Section: Calibration Methods Dlr2008mentioning

confidence: 99%

“…The combination of the publications (King and Myers, 1997;King et al, 1998;Augustyn et al, 2004;Vignola, 2006) provide a set of functions which use the ambient temperature, solar zenith angle, air mass, GHI and DHI as input parameters. Geuder et al (2008) introduced a separate set of correction functions which uses an additional spectral parameter determined from GHI, DHI, and DNI. An improved version of these corrections has been analyzed in .…”

Section: Introductionmentioning

confidence: 99%

Calibration methods for rotating shadowband irradiometers and optimizing the calibration duration

Jessen

Wilbert²,

Nouri³

et al. 2016

Atmos. Meas. Tech.

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Abstract. Resource assessment for concentrated solar power (CSP) needs accurate direct normal irradiance (DNI) measurements. An option for such measurement campaigns is the use of thoroughly calibrated rotating shadowband irradiometers (RSIs). Calibration of RSIs and Si-sensors is complex because of the inhomogeneous spectral response of these sensors and incorporates the use of several correction functions. One calibration for a given atmospheric condition and air mass might not be suitable under different conditions. This paper covers procedures and requirements of two calibration methods for the calibration of rotating shadowband irradiometers. The necessary duration of acquisition of test measurements is examined with regard to the site-specific conditions at Plataforma Solar de Almería (PSA) in Spain. Seven data sets of long-term test measurements were collected. For each data set, calibration results of varying durations were compared to its respective long-term result. Our findings show that seasonal changes of environmental conditions are causing small but noticeable fluctuation of calibration results. Calibration results within certain periods (i.e. November to January and April to May) show a higher likelihood of deviation. These effects can partially be attenuated by including more measurements from outside these periods.Consequently, the duration of calibrations at PSA can now be selected depending on the time of year in which measurements commence.

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A Comprehensive Study of Ground Measurement and Satellite‐Derived Data of Global and Diffuse Radiation

Mahima

Karakoti

Pathak

et al. 2018

Env Prog and Sustain Energy

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The comparative study of measured and calculated data of India Meteorological Department (IMD) and National Aeronautics and Space Administration (NASA) for 23 locations of India for 15 years (1986–2000) is presented. Global and diffuse radiation data are used in the study and to make the study comprehensive, three statistical predictors, viz., mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE) are used for the comparison. The results show that for global radiation, MPE is maximum of −36.65% for Shillong and minimum of 0.93% for New Delhi. MBE is found to be maximum of 1.63 kWh/m2 for Shillong while it is minimum (0.04 kWh/m2) for Nagpur. RMSE is maximum (2.14 kWh/m2) for Shillong and minimum for Ranchi (0.19 kWh/m2). For diffuse radiation, MPE is highest for Bhavnagar (100%) and lowest for Hyderabad (81.33%). MBE is maximum of −7.73 kWh/m2 and minimum of −4.62 kWh/m2 for Srinagar. RMSE is maximum for Bhavnagar (8.26 kWh/m2) and minimum for Srinagar (4.73 kWh/m2). © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13060, 2019

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Corrections for rotating shadowband pyranometers for solar resource assessment

Cited by 20 publications

References 0 publications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Calibration methods for rotating shadowband irradiometers and optimizing the calibration duration

A Comprehensive Study of Ground Measurement and Satellite‐Derived Data of Global and Diffuse Radiation

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