Evaluation of Global Horizontal Irradiance to Plane-of-Array Irradiance Models at Locations Across the United States

Lave, Matthew; Hayes, William; Pohl, Andrew; Hansen, Clifford W.

doi:10.1109/jphotov.2015.2392938

Cited by 126 publications

(72 citation statements)

References 14 publications

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“…These models estimate DNI values larger than those measured, but they do not pose extreme behaviors, in contrast to those in the first and the second groups. Lave et al [23] demonstrated that the Reindl-2 and the Maxwell models use the term of cos θ z (via air mass in the Maxwell model) in addition to k t and, thus, they outperform other models that use k t only, which is consistent with this study. The observation frequency, normalized by dividing with the total number of data points, was calculated according to the level of the DNI.…”

Section: Evaluation Of Existing Solar Radiation Modelssupporting

confidence: 88%

“…Out of many decomposition models, ten were selected in this study: Orgill and Hollands [16], Vignola and McDaniels [17], Louche et al [18], Lee et al [8], Lam and Li [19], CIBSE [20], Erbs et al [21], Maxwell [13], and two from Reindl et al [22]. These models are widely used for modeling solar radiation, e.g., in relevant textbooks [1] and in model comparison studies [5,8,23]. The selected models were evaluated by comparing the modeled and the measured DNI data.…”

Section: Evaluation Of Existing Solar Radiation Modelsmentioning

confidence: 99%

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Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea

2017

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Reliable solar radiation data are important for energy simulations in buildings and solar energy systems. Although direct normal irradiance (DNI) is required for simulations, in addition to global horizontal irradiance (GHI), a lack of DNI measurement data is quite often due to high cost and maintenance. Solar radiation models are widely used in order to overcome the limitation, but only a few studies have been devoted to solar radiation data and modeling in Korea. This study investigates the most suitable solar radiation model that converts GHI into DNI for Korea, using measurement data of the city of Daejeon from 2007 to 2009. After ten existing models were evaluated, the Reindl-2 model was selected as the best. A new model was developed for further improvement, and it substantially decreased estimation errors compared to the ten investigated models. The new model was also evaluated for nine major cities other than Daejeon from the standpoint of typical meteorological year (TMY) data, and consistent evaluation results confirmed that the new model is reliably applicable across Korea.

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Section: Evaluation Of Existing Solar Radiation Modelssupporting

confidence: 88%

Section: Evaluation Of Existing Solar Radiation Modelsmentioning

confidence: 99%

Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea

2017

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“…Validation studies of these transposition models are provided by Behr (1997); David, Lauret, and Boland (2013);Gueymard (2009);Ineichen (2011);and Kambezidis et al (1994). The validation of combined separation and transposition models has been undertaken by Gueymard (2009);Orehounig, Dervishi, and Mahdavi (2014); Lave et al (2015);.…”

Section: Post-processing To Derive Additional Parametersmentioning

confidence: 99%

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

2017

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Designing, financing, and operating successful solar heating, concentrating solar power, and photovoltaic systems requires reliable information about the solar resource available and its variability over time. In the past, seasonal and daily variability has been studied and understood; however, with new solar technologies becoming more important in energy supply grids, small time-scale effects are critical to successful deployment of these important low carbon technologies. A vital part of the bankability of solar projects is to understand the variability of the solar resource so that supply and storage technologies can be optimized.This handbook is the result of 10 years of international collaboration carried out by experts from the International Energy Agency's (IEA's) Solar Heating and Cooling (SHC), Solar PACES, and Photovoltaic Power Systems Technology Collaboration Programmes. Under IEA SHC Task 46: Solar Resource Assessment and Forecasting, experts from 11 countries produced information products and best practices on solar energy resources that will greatly benefit project developers and system operators as well as assist policymakers in advancing renewable energy programs worldwide.Meteorologists, mathematicians, solar technology specialists, and other key solar resource experts from around the world joined forces to further our understanding of the sun's temporal and spatial variability through benchmarking satellite-derived solar resource data and solar forecasts, developing best practices for measuring the solar resource, and conducting research to improve satellite-based algorithms. The results of IEA SHC Task 46 are useful to a wide range of users of solar heating and cooling, photovoltaics, and concentrating solar power systems and of building developers and owners as well as anyone else who needs to understand and predict sunlight for agricultural or other purposes.The earlier edition of the handbook, which was published in 2015, is used worldwide as a reference for each stage of a solar energy project. Since that time, there has been substantial growth in the interest in high-quality "bankable" solar resource data. This revision adds significant new methods so it will be even more useful.This publication is a summary that details the fundamentals of solar resources as well as captures the state of the art. For those wanting more depth, it also provides the references where more detailed information can be found.I would like to acknowledge the leadership of the National Renewable Energy Laboratory and express appreciation to the U.S. Department of Energy for producing the handbook and incorporating results from IEA SHC Task 46. Ken Guthrie Chair, IEA Solar Heating and Cooling Technology Collaboration Programme June 2017This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. Preface Dave Renné Dave Renné Renewables, LLCAs the world looks for carbon-free sources to meet final energy demand associated with heat, electrical power, and transport, ...

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“…However, in most cases, the fixed tilted plane is used because of lower cost of installation and maintenance. Nevertheless, data on solar radiation are generally provided for horizontal planes [5][6][7][8]. In this wise, algorithms and mathematical models are used to transpose the available solar radiation data on horizontal plane to the solar radiation on the tilted plane.…”

Section: Introductionmentioning

confidence: 99%

“…In this wise, algorithms and mathematical models are used to transpose the available solar radiation data on horizontal plane to the solar radiation on the tilted plane. Such mathematical models are referred to as transposition model [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Determination of Yearly Fixed Optimal Tilt Angle for Flat-Plate Photovoltaic Modules Based on Perez Transposition Model

Ekanem¹

2017

AJSEA

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Abstract:In this paper, a method for the determination of the optimal tilt angle for yearly fixed flat-plate photovoltaic (PV) module at any given location is presented. The method is based on yearly global radiation incident on a horizontal plane as downloaded from NASA website. Futrthermore, PVSyst software that uses transposition model is used to generate the yearly global radiation incident on a tilted plane for various tilt angles, from 0° to 46°. The study is conducted for a health facility in Uyo, Akwa Ibom state, Nigeria with longitude of 7.860761, latitude of 5.011474 and elevation of 67.506 m. The optimal tilt angle is obtained from the quadratic trendline equation fitted to the graph of the transposition factor versus tilt angle. The result is that the optimal tilt angle for the yearly fixed flat-plate PV module at the selected Flocation is 9.71° which gives average yearly transposition factor 1.0105. Essential, the results indicate that about additional 1.05% of solar radiation will be captured per year by tilting the PV module at optimal tilt angle of 9.71°. At any other tilt angle less solar radiation will be captured per year.

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Evaluation of Global Horizontal Irradiance to Plane-of-Array Irradiance Models at Locations Across the United States

Cited by 126 publications

References 14 publications

Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea

Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea

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

Determination of Yearly Fixed Optimal Tilt Angle for Flat-Plate Photovoltaic Modules Based on Perez Transposition Model

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