Evaluation of the National Solar Radiation Database (NSRDB): 1998-2015

Habte, Aron; Sengupta, Manajit; Lopez, Anthony

doi:10.2172/1351858

Cited by 53 publications

(46 citation statements)

References 9 publications

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“…The satellite pixel represents a certain area, typically 1-100 km 2 . Depending on that size, some subpixel variability and cloudinduced parallax effects may contribute to higher random errors in both GHI and DNI, as suggested by previous studies (e.g., Habte et al 2017;Cebecauer et al 2011a;. The resolution of satellite images has limits to adequately describe properties of small and scattered clouds in the case of intermittent cloud situations.…”

Section: Challenges With Modeled Data Uncertainty Estimationmentioning

confidence: 93%

“…Recent studies, such as those by Habte et al (2017), Šúri and Cebecauer (2014), Wilcox (2012), or Cebecauer et al (2011a, discussed quantification methods aimed at a comprehensive representation of the model uncertainty using the GUM method. This implements the error statistics (bias, RMSE, and uncertainty) of those ground-based irradiance measurements used to evaluate the modeled data.…”

Section: -11mentioning

confidence: 99%

“…As shown in Figure 6-3, an uncertainty of 5% was chosen for the measurements and kept constant throughout the averaging time because the main uncertainty of radiometers (due to calibration) did not change with averaging time (Habte et al 2017;Reda 2011). The relative bias and RMSE associated with Figure 6-3 are shown in Figure 6-4 (left and right, respectively).…”

Section: -12mentioning

confidence: 99%

“…Inter-annual variability in modeled NSRDB data (1998-2015) and in ground-based irradiance measurements at seven SURFRAD sites using the coefficient of variation (COV) metric Habte et al (2017) calculated an inter-annual variability metric using Equations 6-9 and 6-10.…”

Section: -13mentioning

confidence: 99%

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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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Section: Challenges With Modeled Data Uncertainty Estimationmentioning

confidence: 93%

Section: -11mentioning

confidence: 99%

Section: -12mentioning

confidence: 99%

Section: -13mentioning

confidence: 99%

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

2017

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“…Fortunately, the methodology for clear‐sky filtering based on time series irradiance data is well established in the literature. For the analysis that follows, time series irradiance data for each PV site are downloaded from the National Solar Radiation Database (NSRDB) . NSRDB is specifically chosen over site irradiance data because in many cases, the site irradiance instruments are mounted on the trackers that are under investigation.…”

Section: Methods For Determining Daily Tracker Functionalitymentioning

confidence: 99%

A methodology to analyze photovoltaic tracker uptime

Ruth

Muller

2018

Progress in Photovoltaics

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A metric is developed to analyze the daily performance of single‐axis photovoltaic (PV) trackers. The metric relies on comparing correlations between the daily time series of the PV power output and an array of simulated plane‐of‐array irradiances for the given day. Mathematical thresholds and a logic sequence are presented, so the daily tracking metric can be applied in an automated fashion on large‐scale PV systems. The results of applying the metric are visually examined against the time series of the power output data for a large number of days and for various systems. The visual inspection results suggest that overall, the algorithm is accurate in identifying stuck or functioning trackers on clear‐sky days. Visual inspection also shows that there are days that are not classified by the metric where the power output data may be sufficient to identify a stuck tracker. Based on the daily tracking metric, uptime results are calculated for 83 different inverters at 34 PV sites. The mean tracker uptime is calculated at 99% based on 2 different calculation methods. The daily tracking metric clearly has limitations, but as there is no existing metrics in the literature, it provides a valuable tool for flagging stuck trackers.

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Wind and solar resource data sets

Clifton

Draxl

Badger

et al. 2017

WIREs Energy & Environment

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The range of resource data sets spans from static cartography showing the mean annual wind speed or solar irradiance across a region to high temporal and high spatial resolution products that provide detailed information at a potential wind or solar energy facility. These data sets are used to support continental‐scale, national, or regional renewable energy development; facilitate prospecting by developers; and enable grid integration studies. This review first provides an introduction to the wind and solar resource data sets, then provides an overview of the common methods used for their creation and validation. A brief history of wind and solar resource data sets is then presented, followed by areas for future research. WIREs Energy Environ 2018, 7:e276. doi: 10.1002/wene.276 This article is categorized under: Wind Power > Climate and Environment Solar Heating and Cooling > Climate and Environment Energy Infrastructure > Climate and Environment

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Evaluation of the National Solar Radiation Database (NSRDB): 1998-2015

Cited by 53 publications

References 9 publications

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

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

A methodology to analyze photovoltaic tracker uptime

Wind and solar resource data sets

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