Expert Quality Control of Solar Radiation Ground Data Sets

Solar Energy, 2017

Several quality control (QC) procedures are available to detect errors in ground records of solar radiation, mainly range tests, model comparison and graphical analysis, but most of them are ineffective in detecting common problems that generate errors within the physical and statistical acceptance ranges. Herein, we present a novel QC method to detect small deviations from the real irradiance profile. The proposed method compares ground records with estimates from three independent radiation products, mainly satellite-based datasets, and flags periods of consecutive days where the daily deviation of the three products differs from the historical values for that time of the year and region. The confidence intervals of historical values are obtained using robust statistics and errors are subsequently detected with a window function that goes along the whole time series. The method is supplemented with a graphical analysis tool to ease the detection of false alarms. The proposed QC was validated in a dataset of 313 ground stations. Faulty records were detected in 31 stations, even though the dataset had passed the Baseline Surface Radiation Network (BSRN) range tests. The graphical analysis tool facilitated the identification of the most likely causes of these errors, which were classified into operational errors (snow over the sensor, soiling, shading, time shifts, large errors) and equipment errors (miscalibration and sensor replacements), and it also eased the detection of false alarms (16 stations). These results prove that our QC method can overcome the limitations of existing QC tests by detecting common errors that create small deviations in the records and by providing a graphical analysis tool that facilitates and accelerates the inspection of flagged values.

During the past few decades, there has been a continual rise in interest in passive and active solar energy uses, not only in the governmental and commercial sectors, but also within the private sector. There is thus a need for taking measurements of solar irradiation and creating local and regional databases of irradiation and synoptic (meteorological) information. However, there is no guarantee of the quality of the data collected, as often due care is not exercised with respect to quality control of the measured dataset.

Assessment of the solar resource is based upon measured data, where available. However, with any measurement there exist errors. Consequently, solar radiation data do not exhibit necessarily the same reliability and it often happens that users face time series of measurements containing questionable values though preliminary technical control has been done before the data release. To overcome such a situation, a major effort has been undertaken at the Royal Meteorological Institute of Belgium (RMIB) to develop procedures and software for performing post-measurement quality control of solar data from the radiometric stations of our in situ solar monitoring network. Moreover, because solar energy applications usually need continuous time series of solar radiation data, additional procedures have also been established to fill missing values (data initially lacking or removed via quality checks).

Energy Procedia, 2014

In the Solar Radiation Resource Assessment (SRRA) project of the Ministry of New and Renewable Energy, India a network of 51 automatic solar radiation monitoring stations have been set up across India. Such a large number of high-quality solar radiation stations with sensitive instruments require efficient procedures for regularly controlling proper operation of each station, the quality of the measured data, and its overall performance. Following best practices for quality assessment tests, such routines are implemented at the SRRA archiving and processing center. Various quality control tests are applied that check the plausibility of data, differentiate trustworthy data from likely erroneous data and flag them accordingly. A data flagging system is implemented to identify, differentiate and quantify different types of errors. These quality-checked, flagged data are then used by routines to create monthly reports and data products. This paper describes the automated quality check system implemented and evaluates the performance of stations since their erection in 2011. This paper also describes first experiments to validate the functionality of the applied quality checks. The quality flag statistics of all 51 stations reveals that some stations are performing very well and others need more attention to improve. In the period from January 2012 to March 2013 on an average over all 51 stations, 92 % of the solar radiation data are classified as correct. Around 4 % of solar radiation data do not pass the coherence test. Tracking errors are observed during 0.3 % of the time averaged over all 51 stations. This analysis helps to further improve the operation of this network and find ways for better-automatized quality checks.

International Journal of Renewable Energy Development, 2021

Solar irradiance data from high-quality ground-based measurements are primordial for different solar energy applications. In order to achieve the required accuracy, quality control procedures are of great benefit. A variety of approaches have been proposed. In this sense, some approaches propose a visual representation of the routine, while others only provide a time series of binary flag values, and do not propose any specific visualization of the flagged data as opposed to non-flagged ones. In this regard, the present paper puts forward a complete routine including several quality control procedures for solar irradiance measurements by providing visual support for these different approaches. The visual tool in question was validated using five years research data with 10 minutes resolution of the global, diffuse and direct components of solar irradiation collected from three ground-based weather stations in Morocco. This visual tool puts forth a more precise idea of the measurem...

Solar Energy, 2010

Measurements of surface radiation in China are too sparse to meet demand for scientific research and engineering applications. Moreover, the radiation data often include erroneous and questionable values though preliminary quality-check has been done before the data release. Therefore, quality control of radiation data is often a prerequisite for using these data. In this study, a set of quality-check procedures were implemented to control the quality of the solar radiation measurements at 97 stations in China. A hybrid model for estimating global solar radiation was then evaluated against the controlled data. The results show that the model can estimate the global radiation with accuracy of MBE less than 1.5 MJ m À2 and RMSE less than 2.8 MJ m À2 for daily radiation and RMSE less than 2.0 MJ m À2 for monthly-mean daily radiation at individual stations over most of China except at a few stations where unsatisfactory estimates were possibly caused by severe air pollution or too dense clouds. The MBE averaged over all stations are about 0.7 MJ m À2 and RMSE about 2.0 MJ m À2 for daily radiation and RMSE about 1.3 MJ m À2 for monthly-mean daily radiation. Finally, this model was used to fill data gaps and to expand solar radiation data set using routine meteorological station data in China. This data set would substantially contribute to some radiation-related scientific studies and engineering applications in China.

Solar Energy, 2002

The control of the quality of irradiation data is often a prerequisite to their further processing. Though data are usually controlled by meteorological offices, the sources are so numerous that the user often faces time-series of measurements containing questionable values. As ...

Metrologia, 2012

Continuity of the 33-year long total solar irradiance record has been facilitated by corrections for offsets due to calibration differences between instruments, providing a solar data record with precision approaching that needed for Earth climate studies. Recent laboratory tests have 1) improved measurement absolute accuracy to mitigate potential future data gaps, 2) helped explain the causes of instrument offsets, and 3) improved consistency between the international references upon which various instrument calibrations are based. ___________________________________________________________________________________________

2015

Publicly accessible, high-quality, long-term, satellite-based solar resource data is foundational and critical to solar technologies to quantify system output predictions and deploy solar energy technologies in grid-tied systems. Solar radiation models have been in development for more than three decades. For many years, the National Renewable Energy Laboratory (NREL) developed and/or updated such models through the National Solar Radiation Data Base (NSRDB). There are two widely used approaches to derive solar resource data from models: (a) an empirical approach that relates ground-based observations to satellite measurements and (b) a physics-based approach that considers the radiation received at the satellite and creates retrievals to estimate clouds and surface radiation. Although empirical methods have been traditionally used for computing surface radiation, the advent of faster computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) i...