Global Horizontal Irradiance Measurements Research Articles

A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. This study introduces a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL’s Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

Modeling Photosynthetically Active Radiation from Satellite-Derived Estimations over Mainland Spain

A model based on the known high correlation between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI) was implemented to estimate PAR from GHI measurements in this present study. The model has been developed using satellite-derived GHI and PAR estimations. Both variables can be estimated using Kato bands, provided by Satellite Application Facility on Climate Monitoring (CM-SAF), and its ratio may be used as the variable of interest in order to obtain the model. The study area, which was located in mainland Spain, has been split by cluster analysis into regions with similar behavior, according to this ratio. In each of these regions, a regression model estimating PAR from GHI has been developed. According to the analysis, two regions are distinguished in the study area. These regions belong to the two climates dominating the territory: an Oceanic climate on the northern edge; and a Mediterranean climate with hot summer in the rest of the study area. The models obtained for each region have been checked against the ground measurements, providing correlograms with determination coefficients higher than 0.99.

Bias correction of a novel European reanalysis data set for solar energy applications

One of the major challenges during the transition phase of the energy system is to maintain the balance between energy supply and demand. Rising questions are often related to site mapping, variability, extremes and compensation effects for example. A fundamental source of information to answer these questions are high quality data sets of renewable energy related variables. As reanalyses provide all relevant data to assess wind and solar power generation over a long period of time (decades) in a gridded consistent way, they exhibit great potential in the field of renewable energy. A new regional reanalysis is COSMO-REA6, which covers the European domain over the years 1995–2014 with a horizontal resolution of about 6 km and a temporal resolution of 15 min. In this paper, we first assess the quality of the Global Horizontal Irradiance (GHI) provided by COSMO-REA6. High quality GHI measurements obtained through the Baseline Surface Radiation Network (BSRN) are used as reference and reveal systematic short comings in the reanalysis: (1) an underestimation of GHI in clear sky situations and (2) an overestimation of GHI in cloudy sky situations. In order to reduce these systematic regime dependent biases, a post-processing is developed. The applied post-processing method is a scaling based on orthogonal distance regressions for two different regimes, i.e., “clear sky” and “cloudy sky”. The two regimes are distinguished by the use of a transmissivity threshold. The post-processed GHI shows a significant reduction of the systematic biases and an improvement in representing the marginal distributions. A spatial cross-validation shows the applicability to the whole model domain of COSMO-REA6. Moreover, COSMO-REA6 as well as the post-processed GHI data reveal an added-value when compared to global reanalysis ERA-Interim and MERRA-2. The higher resolution reanalysis exhibits a significantly better performance of representing GHI variability, as well as biases, RMSE and other conventional scores. The post-processed GHI data are freely available for download.

SOLAR DEVELOPMENT IN UZBEKISTAN. STEP 1: SOLAR DATA COMPILATION

Solar radiation resource data serve as fundamental information for programs of large-scale deployment of solar energy technologies. While solar resources in the Republic of are estimated to be significant based on limited information, comprehensive understanding requires considerably more data and analysis in order to optimize the process of planning and siting solar energy power plants. This paper highlights the analysis of the four-year of solar resource measurements from new ground meteorological stations in which were installed within the framework of the project of the Asian Bank TA 8008 Development of solar energy in Uzbekistan in 2013.The current study should have significant applications for preliminary technology selection, power plant modeling, and resource forecasting. The analysis uses4-year (January 2013–December 2016) data from 6 stations located in the south-eastern part of the republic based on ten-minute measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters.

Irradiance prediction intervals for PV stochastic generation in microgrid applications

The increasing interest in integrating volatile resources into microgrids implies the necessity of quantifying the uncertainty of photovoltaic (PV) production using dedicated probabilistic forecast techniques. The work presents a novel method to construct ultra-short-term and short-term prediction intervals (PIs) for solar global horizontal irradiance (GHI). The model applies the k-means algorithm to cluster observations of the clear-sky index according to the value of selected data features. At each timestep, the features are compared with the actual conditions to identify the representative cluster. The lower and upper bounds of the PI are calculated as the quantiles of the irradiance instances belonging to the selected cluster at a target confidence level. The validation is performed in 3 datasets of GHI measurements, each one of 85days. The model is able to deliver high performance PIs for forecast horizons ranging from sub-second to intra-hour ahead without the need of additional sensing systems such as all-sky cameras.

Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis

We report on the construction of generic models to calculate photosynthetically active radiation (PAR) from global horizontal irradiance (GHI), and vice versa. Our study took place at stations of the Greek UV network (UVNET) and the Hellenic solar energy network (HNSE) with measurements from NILU-UV multi-filter radiometers and CM pyranometers, chosen due to their long (≈1M record/site) high temporal resolution (≈1min) record that captures a broad range of atmospheric environments and cloudiness conditions. The uncertainty of the PAR measurements is quantified to be ±6.5% while the uncertainty involved in GHI measurements is up to ≈±7% according to the manufacturer. We show how multi-linear regression and nonlinear neural network (NN) models, trained at a calibration site (Thessaloniki) can be made generic provided that the input–output time series are processed with multi-channel singular spectrum analysis (M-SSA). Without M-SSA, both linear and nonlinear models perform well only locally. M-SSA with 50 time-lags is found to be sufficient for identification of trend, periodic and noise components in aerosol, cloud parameters and irradiance, and to construct regularized noise models of PAR from GHI irradiances. Reconstructed PAR and GHI time series capture ≈95% of the variance of the cross-validated target measurements and have median absolute percentage errors <2%. The intra-site median absolute error of M-SSA processed models were ≈8.2±1.7W/m2 for PAR and ≈9.2±4.2W/m2 for GHI. When applying the models trained at Thessaloniki to other stations, the average absolute mean bias between the model estimates and measured values was found to be ≈1.2W/m2 for PAR and ≈0.8W/m2 for GHI. For the models, percentage errors are well within the uncertainty of the measurements at all sites. Generic NN models were found to perform marginally better than their linear counterparts.

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

Identification of periods of clear sky irradiance in time series of GHI measurements

We present a simple algorithm for identifying periods of time with broadband global horizontal irradiance (GHI) similar to that occurring during clear sky conditions from a time series of GHI measurements. Other available methods to identify these periods do so by identifying periods with clear sky conditions using additional measurements, such as direct or diffuse irradiance. Our algorithm compares characteristics of the time series of measured GHI with the output of a clear sky model without requiring additional measurements. We validate our algorithm using data from several locations by comparing our results with those obtained from a clear sky detection algorithm, and with satellite and ground-based sky imagery.

Evaluation of WRF shortwave radiation parameterizations in predicting Global Horizontal Irradiance in Greece

This study aims at assessing the differences induced in the Global Horizontal Irradiance (GHI) predictions by the mesoscale atmospheric Weather Research and Forecasting (WRF) model when using different shortwave radiation. Model predictions are compared with GHI measurements at 12 stations of the Hellenic Network of Solar Energy (HNSE) for January, April, July and October 2013. The shortwave radiation schemes that were evaluated are: the Dudhia, the updated Rapid Radiative Transfer Model (RRTMG), the updated Goddard and the Goddard Fluid Dynamics Laboratory (GFDL) schemes. All schemes perform better under cloudless conditions due to limited ability of the WRF model to simulate cloudy conditions. The Dudhia scheme performs best with mean relative difference of 2.2 ± 15% for clear-skies, while the differences for the other schemes range between 5 and 12% with similar standard deviations. For all-skies, the model-derived hourly GHI is overestimated for all schemes (∼40–70%).For the daily averages, the model predictions are in better agreement with the measurements, mainly under all-sky conditions, with deviations of about half those of the hourly data and smaller standard deviations. There are strong indications that the differences of the model predictions from the measurements depend on the solar zenith angle and the amount of aerosols at each station.

Assessment of estimation methods of DNI resource in solar concentrating systems

The DNI (Direct Normal Irradiance) is the resource utilized by solar concentrators, in CSP and CPV applications. However, it is not often measured due to practical reasons, and is derived instead from measurements of global irradiance or satellite-based models. This paper describes a study of different estimation procedures for the assessment of the DNI, using experimental data with a time scale of 1min, taken at two different latitudes in Italy. The analyzed approaches include measuring techniques and models. Accurate estimations of DNI are obtained as derived from measurements of global horizontal irradiance and diffuse horizontal irradiance if the offset in pyranometers are properly corrected and the zenith angle is not too high. The measured and calculated values of DNI are used to estimate the hourly, daily, monthly and seasonal irradiation. The results show that the different estimation methods can lead to quite different conclusions when comparing short-term and long-term solar radiation availability.

Assessment of solar radiation resources in Saudi Arabia

Solar radiation resource data are the foundation of information for programs of large-scale deployment of solar energy technologies. While the solar resource in Saudi Arabia and the Arabian Peninsula was believed to be significant based on limited past data, understanding the spatial and temporal variability requires significantly more data and analysis in order to optimize planning and siting solar energy power plants. This paper summarizes the analysis of the first year of broadband solar resource measurements from a new monitoring network in Saudi Arabia developed by the King Abdullah City for Atomic and Renewable Energy (K.A.CARE). The analysis used twelve months (October 2013–September 2014) of data from 30 stations distributed across the country based on one-minute measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters. Network design, implementation, and data quality assurance are described to document the network extent and quality. For the 30 stations, the annual average daily GHI ranged from about 5700Wh/m2 to 6700Wh/m2 with consistently higher values inland and lower values along the coasts. This indicates that photovoltaic technologies would perform well at any location although extreme high temperatures (over 30°C annual average in some locations) may degrade the performance of some types of photovoltaic technologies. Annual average daily DNI was much more variable across the stations, ranging from about 4400Wh/m2 to over 7300Wh/m2 with the highest values and clearest skies in the northwest part of the country. While most regions have sufficient solar resources for concentrating solar technologies, the western inland sites with average daily totals of over 6474Wh/m2 (average yearly totals of 2400kWh/m2/year) are superior to the eastern sites with average daily totals closer to 5510Wh/m2 (average yearly totals of 2000kWh/m2/year). This first year of data represents the beginning of a deeper understanding of solar resource characteristics in Saudi Arabia and the Middle East. Although continued measurements are needed to understand the interannual resource variability, the current study should have significant applications for preliminary technology selection, power plant modeling, and resource forecasting.

Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index

The cloud index is a key parameter of the Heliosat method. This method is widely used to calculate solar irradiance on the Earth’s surface from Meteosat visible channel images. Moreover, cloud index images are the basis of short-term forecasting of solar irradiance and photovoltaic power production. For this purpose, cloud motion vectors are derived from consecutive images, and the motion of clouds is extrapolated to obtain forecasted cloud index images. The cloud index calculation is restricted to the daylight hours, as long as SEVIRI HR-VIS images are used. Hence, this forecast method cannot be used before sunrise. In this paper, a method is introduced that can be utilized a few hours before sunrise. The cloud information is gained from the brightness temperature difference (BTD) of the 10.8 µm and 3.9 µm SEVIRI infrared channels. A statistical relation is developed to assign a cloud index value to either the BTD or the brightness temperature T10:8, depending on the cloud class to which the pixel belongs (fog and low stratus, clouds with temperatures less than 232 K, other clouds). Images are composed of regular HR-VIS cloud index values that are used to the east of the terminator and of nighttime BTD-derived cloud index values used to the west of the terminator, where the Sun has not yet risen. The motion vector algorithm is applied to the images and delivers a forecast of irradiance at sunrise and in the morning. The forecasted irradiance is validated with ground measurements of global horizontal irradiance, and the advantage of the new approach is shown. The RMSE of forecasted irradiance based on the presented nighttime cloud index for the morning hours is between 3 and 70 W/m2, depending on the time of day. This is an improvement against the previous precision range of the forecast based on the daytime cloud index between 70 and 85 W/m2.

Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia

Separating global horizontal irradiance measurements into direct and diffuse components has been vigorously discussed over the past half-century of solar radiation research leading to the creation of many models which attempt to compute these components with varying degrees of success. However, over the course of this discussion, nearly all studies have focused on hourly values, with no studies that have proposed a model for minute-level values of irradiance. As data-logging technologies have become much more prolific and their storage capabilities much larger, solar radiation monitoring sites are more commonly logging data at intervals much less than one hour, but no models exists that are designed to separate these measurements into direct and diffuse components. In Australia, the Australian Bureau of Meteorology and the Australian Solar Institute have compiled a dataset of tens of millions of one-minute global, direct and diffuse solar irradiance observations, comprising data from regions all around Australia. This dataset provides a unique opportunity to investigate the relationships between global irradiance and its direct and diffuse components at higher resolution than has previously been possible. Herein, the largest and most complete diffuse fraction model analysis yet undertaken for Australian solar radiation data, and the first ever to focus on minute resolution data is reported. Nine of the most prominent diffuse fraction, or “separation”, models are tested against minute resolution radiation data from three datasets. The first removed cloud enhancement events in accordance with practices undertaken by the majority of studies in the literature. The second retains these events in order to assess which model would be best suited for operational purposes. The third consisted of only clear sky observations, in order to assess the performance of diffuse fraction models under clear skies. Through the course of this study only the Perez model was found to perform satisfactorily for minute resolution data at sites in southeastern Australia. Three new diffuse models proposed in this study, one trained for each of the three datasets, were found to greatly exceed the performance of existing modeling techniques, with slight improvements over the Perez model.

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

We report an evaluation of the accuracy of combinations of models that estimate plane-of-array (POA) irradiance from measured global horizontal irradiance (GHI). This estimation involves two steps: 1) decomposition of GHI into direct and diffuse horizontal components and 2) transposition of direct and diffuse horizontal irradiance (DHI) to POA irradiance. Measured GHI and coincident measured POA irradiance from a variety of climates within the United States were used to evaluate combinations of decomposition and transposition models. A few locations also had DHI measurements, allowing for decoupled analysis of either the decomposition or the transposition models alone. Results suggest that decomposition models had mean bias differences (modeled versus measured) that vary with climate. Transposition model mean bias differences depended more on the model than the location. When only GHI measurements were available and combinations of decomposition and transposition models were considered, the smallest mean bias differences were typically found for combinations which included the Hay/Davies transposition model.

Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium

Solar energy production is directly correlated to the amount of radiation received at a given location. Appropriate information on solar resources is therefore very important for designing and sizing solar energy systems. Concentrated solar power projects and photovoltaic tracking systems rely predominantly on direct normal irradiance (DNI). However, the availability of DNI measurements from surface observation stations has proven to be spatially too sparse to quantify solar resources at most potential sites. Satellite data can be used to calculate estimates of direct solar radiation where ground measurements do not exist. Performance of decomposition models of various complexity have been evaluated against one year of in situ observations recorded on the roof of the radiometric tower of the Royal Meteorological Institute of Belgium in Uccle, Brussels. Models were first evaluated on a hourly and sub-hourly basis using measurements of global horizontal irradiance (GHI) as input. Second, the best performing ground-based decomposition models were used to extract the direct component of the global radiation retrieved from Meteosat Second Generation (MSG) images. Results were then compared to direct beam estimations provided by satellite-based diffuse fraction models and evaluated against direct solar radiation data measured at Uccle. Our analysis indicates that valuable DNI estimation can be derived from MSG images over Belgium regardless of the satellite retrieved GHI accuracy. Moreover, the DNI retrieval from MSG data can be implemented on an operational basis.

Forecasting of Global Horizontal Irradiance Using Sky Cover Indices

This work discusses the relevance of three sky cover (SC) indices for solar radiation modeling and forecasting. The three indices are global in the sense that they integrate relevant information from the whole sky and thus encode cloud cover information. However, the three indices also emphasize different specific meteorological processes and sky radiosity components. The three indices are derived from the observed cloud cover via total sky imager (TSI), via measurements of the infrared radiation (IR), and via pyranometer measurements of global horizontal irradiance (GHI). We enhance the correlations between these three indices by choosing optimal expressions that are benchmarked against the GHI SC index. The similarity of the three indices allows for a good qualitative approximation of the GHI irradiance when using any of the other two indices. An artificial neural network (ANN) algorithm is employed to improve the quantitative modeling of the GHI sky cover index, thus improving significantly the forecasting details of GHI when all three indices are used.

Assessment and recalibration of the Heliosat-2 method in global horizontal irradiance modeling over the desert environment of the UAE

Remote estimation of global horizontal irradiance (GHI) by Heliosat-2 model has been benchmarked against ground-based measurements in different locations. The obtained results have shown that the level of agreement between ground-based and model-based GHI values are location dependent. To our knowledge no similar studies have been carried out over the Arabian Peninsula. The unique climatic condition in the Peninsula, characterized by high concentrations of airborne dust particles and high humidity makes the region a particularly interesting case.In this study Heliosat-2 method was used to derive the ground surface GHI in the United Arab Emirates. Inputs to the model were monthly Linke turbidity factor normalized to an air mass of 2 and a cloud index derived from SEVIRI sensor onboard the European satellite Meteosat. The Linke turbidity factor was obtained from a ground network of seven stations distributed across the UAE. A SEVIRI-based technique was developed and used to derive cloud index from high-resolution visible channels. Ground surface GHI measurements were collected from four inland stations for a period ranging from mid-2007 to mid-2010. The obtained results show that the Heliosat-2 model underestimates the GHI. The obtained root mean square error (RMSE) and mean bias error (MBE) values ranged from 16.3% to 18.5% and −13.6% to −15.8%, respectively. A constant bias was observed between modeled and measured GHI throughout the four stations. To correct this bias, the empirical equation used in Heliosat-2 to estimate the clear sky diffuse horizontal irradiance (DHIclear) was recalibrated. With the new DHIclear empirical equation, the modified Heliosat-2 model becomes more adapted to desert and dusty environments such as that of the UAE. By applying the modified DHIclear equation, the RMSE and MBE values dropped to 9.5–10.3% and −1.2 to +0.8%, respectively.

The Estimation of Clear Sky Global Horizontal Irradiance at the Equator

We analyse Singapore global horizontal irradiance (GHI) measurements to estimate clear sky GHI in the equator region. The data consists of a one-year period of clear sky GHI at three solar irradiance monitoring stations in Singapore, namely, First Zero Energy House in Singapore (1.3094∘N, 103.9160∘E) located in the east of the country, Solar Energy Research Institute of Singapore (1.3007∘N, 103.7718∘E) in the mid-west, and Nanyang Technology University Intelligence Systems Center (1.3436∘N, 103.6792∘E) in the west. Several empirical clear sky models are considered for a Singapore case study. An regression method is proposed to parameterise the model of choice for Singapore's measured GHI. The developed model is validated using GHI measurements from difference stations over different time periods.