Global Radiation Data Research Articles

Clustering of visible and infrared solar irradiance for solar architecture design and analysis

Incoming solar radiation is a key factor influencing solar architecture design. It determines the thermal and optical regime of the building envelope and affects the solar heat and light transfer between the indoors and outdoors. Computational analysis is an essential tool in solar architecture design. Usually, an entire year’s weather data in a conventional weather file can be imported into such computational analyses. Solar irradiance data used in a conventional solar architecture design analytics are broadband (the total of UV, VIS, and NIR); however, these three components play different roles in building energy efficiency. So, analyzing individual solar components separately can be desirable. This research is to develop estimation models of the VIS and NIR components that can be captured efficiently from readily available datasets collected from the ground weather stations; such a model can then be conveniently implemented into current solar architecture design and research. We explored and tested classification-based modeling methods for decomposing hourly broadband global horizontal solar irradiance data in conventional weather files into hourly global horizontal solar VIS and NIR components. Furthermore, a workflow of how to implement these models in solar architecture design and analysis has been developed and discussed herein.

Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data

This study provides several models for accurately computing the annual optimum tilt angle for fixed solar photovoltaic arrays or solar collectors, in any location of the world. The optimum tilt angle that maximizes the annual energy yield can therefore be easily calculated in the absence of meteorological data and simulation software tools. The proposed models are calculated using global horizontal radiation data collected from 2551 sites across the world. In the process, well-established submodels have been selected to estimate the hourly irradiance on any possible inclined surface, and its corresponding annual energy yield. After selecting the optimum angle for each location, through a regression analysis, a mathematical model that calculates annual optimum angles as a function of latitude has been developed. Furthermore, regression techniques such as neural networks and decision trees have been compared with the polynomial models. Finally, the results are compared to those obtained from high-quality 1-min measured irradiance data obtained at 52 research-class stations from the World Radiation Monitoring Center–Baseline Surface Radiation Network, providing a remarkably high number of validation data points. The results are analyzed, validated, and compared with previous research proposals proving the good performance of the proposed models.

Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridization approach

The unsteady and intermittent feature (mainly due to atmospheric mechanisms and diurnal cycles) of solar energy resource is often a stumbling block, due to its unpredictable nature, to receiving high-intensity levels of solar radiation at ground level. Hence, there has been a growing demand for accurate solar irradiance forecasts that properly explain the mixture of deterministic and stochastic characteristic (which may be linear or nonlinear) in which solar radiation presents itself on the earth’s surface. The seasonal autoregressive integrated moving average (SARIMA) models are popular for accurately modelling linearity, whilst the neural networks effectively capture the aspect of nonlinearity embedded in solar radiation data at ground level. This comparative study couples sinusoidal predictors at specified harmonic frequencies with SARIMA models, neural network autoregression (NNAR) models and the hybrid (SARIMA-NNAR) models to form the respective harmonically coupled models, namely, HCSARIMA models, HCNNAR models and HCSARIMA-NNAR models, with the sinusoidal predictor function, SARIMA, and NNAR parts capturing the deterministic, linear and nonlinear components, respectively. These models are used to forecast 10-minutely and 60-minutely averaged global horizontal irradiance data series obtained from the RVD Richtersveld solar radiometric station in the Northern Cape, South Africa. The forecasting accuracy of the three above-mentioned models is undertaken based on the relative mean square error, mean absolute error and mean absolute percentage error. The HCNNAR model and HCSARIMA-NNAR model gave more accurate forecasting results for 60-minutely and 10-minutely data, respectively.
 Highlights
 
 HCSARIMA models were outperformed by both HCNNAR models and HCSARIMA-NNAR models in the forecasting arena.
 HCNNAR models were most appropriate for forecasting larger time scales (i.e. 60-minutely).
 HCSARIMA-NNAR models were most appropriate for forecasting smaller time scales (i.e. 10-minutely).
 Models fitted on the January data series performed better than those fitted on the June data series.

Daily spectral ocean surface albedo (OSA) parameterization in case of clearness index (Kt) and phytoplankton variability in Malacca Strait

In this study, we examined the albedo's dependence on clearness index (Kt) based on measured global radiation data and phytoplankton variability influence represented by chlorophyll (a) concentration at the upper layers over the Malacca Strait. Half-hourly global solar radiation (5°26′53″ N, 100°11′36″ E) measurement over the period from January to October 2017 was analyzed to investigate the correlation between clearness index and ocean surface albedo (OSA) in case of phytoplankton influence in Malacca Strait coastal waters. Three days (January 29, February 26 and March 22, 2017) with high irradiation values were selected for daily OSA variability study. The parameterization includes bio-optical analytical modelling based on apparent optical properties and photosynthetically active range (PAR) estimation. Daily timescale analysis of spectral albedo in Malacca Strait enables detail examination for the chlorophyll maximum absorption wavelength (λ = 440 nm) parameterization. OSA diurnal variability was estimated using Jin et al. scheme, which is based on the separation of albedo components (direct and diffuse). Hourly changes in radiation (including Kt) and solar position properties provide valuable information to investigate the pathways of daily spectral albedo in case of small concentrations of chlorophyll (a) and clearness index parameterization.

Power loss minimization in radial distribution systems with obstructed solar astronomical model and temperature effect using grey wolf optimization technique

ABSTRACT This article presents implementation of a solar energy-based DG (Distribution Generation) system, which is optimally designed by taking combined effect of solar irradiation and temperature into consideration to minimize the power loss in the Radial Distribution Systems (RDS). A power gap of 35 W/panel is reduced when the solar panel is considered under ambient temperature effect. Novelty of this work lies in the implementation of the obstructed solar astronomical model to improve the accuracy in the prediction of radiation level. In this work, the considered radiation level is determined to be 14.86% of the global radiation data. Consideration of self-shadowing losses for maximum efficiency from PV panels is another significant aspect of this work. Two different test systems are designed by integrating the proposed DG system to IEEE-15 and IEEE-28 RDS. Load Impedance Matrix (LIM) method is adopted to perform load flow in above mentioned test systems due to its single step analysis with faster results unlike traditional Backward Forward Sweep (BFS) method. Grey Wolf Optimization (GWO) technique is used to obtain optimal location of DG for both the test systems for its simple evaluation, good flexibility, avoidance of local optimal point and derivation free tool, which makes this technique popular among the other heuristic methods. Following DG allocation in IEEE-15 bus RDS, it is observed that active power loss is reduced by 39.69%. Similarly, for IEEE-28 bus RDS, reduction in active power loss is found to be 41.20% after integration of a DG unit. There is also a remarkable improvement in voltage profile in both systems after integration of DG units.

Stabilizing solar farm power output by micro gas turbine integration: Emission performance

Worldwide growth of Photovoltaics (PV) technology has been close to exponential rate in the past decades. However, the increase of power generation by PV imposes new challenges on the existing electrical grid due to the unpredictability of PV power output that depends mainly on solar irradiance. One of the solutions to stabilize solar farm power output fluctuation by hybridizing Micro Gas Turbines (MGTs) that is controlled to meet the total power production target. Thus, the objective of this paper is to investigate the effect of different operation strategies on the environmental performance on MGT integrated solar farm. Different operation strategies for MGT integrated solar farm have been implemented to constantly produce a total power output of 800 kW throughout the year. The three strategies then compared by analysing emission produced by MGTs. A simulation was conducted in Simulink environment using real ambient temperature and global sun irradiance data. It was found that Operation Strategy 3 reduced carbon dioxide (CO2) by 13.8%, nitrogen oxides (NOx) emission by 3.2%, and carbon monoxide (CO) by 97.9%. This shows that the proposed strategy could stabilize the total power output of the solar farm at minimum emission level.

A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application

The increasing integration of photovoltaic (PV) power plants into power systems demands a high accuracy of yield prediction and measurement. With this aim, different global horizontal irradiance (GHI) estimations based on new-generation geostationary satellites have been recently proposed, providing a growing number of solutions and databases, mostly available online, in addition to the many ground-based irradiance data installations currently available. According to the specific literature, there is a lack of agreement in validation strategies for a bankable, satellite-derived irradiance dataset. Moreover, different irradiance data sources are compared in recent contributions based on a diversity of arbitrary metrics. Under this framework, this paper describes a characterization of metrics based on a principal component analysis (PCA) application to classify such metrics, aiming to provide non-redundant and complementary information. Therefore, different groups of metrics are identified by applying the PCA process, allowing us to compare, in a more extensive way, different irradiance data sources and exploring and identifying their differences. The methodology has been evaluated using satellite-based and ground-measured GHI data collected for one year in seven different Spanish locations, with a one-hour sample time. Data characterization, results, and a discussion about the suitability of the proposed methodology are also included in the paper.

Empirical Models for the Estimation of Solar Sky-Diffuse Radiation. A Review and Experimental Analysis

Accurate solar radiation data are essential for the development of solar energy application systems. The limited availability of solar radiation data, and especially diffuse irradiance values, makes it vital to develop models to estimate these data. The development of estimation models has been the objective of many studies. This paper presents an extended review of the diffuse ratio (k) vs. clearness index (kt) annual, monthly, daily, and hourly frequency regression models. It is however interesting to note that there is a dearth of such knowledge for diffuse ratio–clearness index regressions that are based on averaged data. Monthly-averaged daily global irradiation data are now easily available from the NASA website for any global location. Using existing models, it is possible to decompose the daily to averaged-hourly global irradiation values. The missing link so far has been hourly averaged diffuse irradiation. This article presents regression equations which could be used to estimate that information. For this purpose, hourly global and diffuse irradiation data was pooled from 19 different locations to obtain three latitude-dependent regression models relating the monthly-averaged hourly diffuse ratio ( k ¯ ) to the clearness index ( k t ¯ ). The results show a high relationship between both variables. These regression equations could be used to estimate the averaged diffuse irradiation values from averaged global irradiation values, which are more easily available.

Updating Algerian solar atlas using MEERA-2 data source

According to its geographical location, Algeria holds one of the highest solar potential in the Mediterranean basin, as indicated in many studies. Unfortunately, an important lack of data of the solar global radiation is observed. With a huge area of 2 381 741 km2, it is necessary to have numerous stations performing radiation measurement in order to quantify in an objective way the solar potential. Previously, several studies have been conducted dealing with solar potential evaluation. In this study, an update of the Algerian solar atlas is presented. Thus, a solar mapping on the national territory was carried out based this time on data measured over a period of almost 37 years. In fact MEERA-2 ”The Modern-Era Retrospective Analysis for Research and Applications, Version 2, NASA”, were used in this study.Therefore, solar maps for solar radiation on different tilting planes were plotted thanks to the use of SURFER® software, through Kriging interpolation method. Furthermore, measured data for five (05) locations across the country were used in order to make comparison to those given by MERRA-2.

Capturing Long-Range Dependence and Harmonic Phenomena in 24-Hour Solar Irradiance Forecasting: A Quantile Regression Robustification via Forecasts Combination Approach

The global horizontal irradiance data recorded at the earth’s horizontal surface is a mixture of deterministic (extra-terrestrially) and stochastic components due to the ever-changing atmospheric conditions. The Box-Jenkins short memory stochastic models and their hybrid versions have been used successfully to forecast solar irradiance data. However, these models lack robustness and faulter for distant horizon forecasting such as more than 2 hours in the hourly case. Using a quantile regression model as both a robust benchmark and robustification model, we remedy this drawback by using long memory models and their hybrid versions in 24 hour ahead irradiance forecasting at three sites in South Africa (RVD, UPR and SUN). Results show that the inherent long memory is anti-persistent in all models but one that shows persistence. The forecasts from these long memory-based models are generally within the prediction interval of the benchmark model forecasts for two sites. Based on the RMSE, rRMSE, MAE, rMAE and the pinball loss function at the central quantile level and two extreme quantile levels, forecasts obtained through robustification via forecasts combinations with the quantile regression yield the best performance. The predictive ability of the best model before forecasts combination against the quantile regression model combined (robustified) model based Diebold-Mariano test rejected the null hypothesis of the same accuracy. As a follow up to distinguish which has a superior predictive accuracy, “murphy diagrams” were used. For the RVD and UPR sites, the model based on forecast combination has the best predictive accuracy while the quantile regression based benchmark model has the best accuracy for the SUN data.

Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

The operations of power systems are becoming more challenging on account of the high penetration of renewable power generation, including photovoltaic systems. One method for improving the power system operation involves making accurate forecasts of day-ahead solar irradiation, enabling operators to minimize uncertainty in managing the balance between generation and load. To overcome the limitations of Long Short-term Memory (LSTM) in a one-dimensional forecasting problem, this work proposes a novel method in forecasting solar irradiation by encoding time-series data into images using the Gramian Angular Field and the Convolutional LSTM (ConvLSTM) network. The pre-processed data become a five-dimensional input tensor that is perfectly suitable for ConvLSTM. The ConvLSTM network uses convolution operations in its input-to-state transition and state-to-state transition. The network thus enables time-series forecasting by a feature-rich approach, which ultimately provides competitive forecasting performance despite the use of a small dataset. The proposed method was evaluated in day-ahead solar irradiation forecasting using a univariate dataset of Global Horizontal Irradiation (GHI) data from Fuhai in Taiwan. The proposed method was resampled using 5×2-fold cross-validation, and assessed using the Wilcoxon signed-rank test to determine the statistical significance of the result. It outperformed benchmark methods such as Autoregressive Integrated Moving Average (ARIMA), Convolutional Neural Network cascaded with Long Short-term Memory (CNN-LSTM), and LSTM cascaded with a fully-connected (FC) network.

Low Cost Solar Powered Telecenters for Malaysian Rural Areas: Case Study in Pos Sinderut, Pahang, Malaysia

The main objective of this project is to design and analyze the cost benefits solar PV system for rural telecenter. The project focused on designing the main solar system and solar panel energy performance based on translucency. The task that was carried out in this project was the selection of the suitable system that will suit the operation well while keeping the design cost as low as possible. In this project, the design of the solar panel system was focused on the electric loads and its cost benefit. Homer software was used to evaluate the solar panel system. Meant for ways of communications for indigenous people, the Solar Photovoltaic system is one of the popular solution for off-grid rural community power supply. The optimized combination for a particular rural site can be predicted based on NASA’s data of Solar Global Horizontal Irradiance (GHI). In this project, Pos Sinderut was chosen as the site, and the data load was measured. In our preliminary findings, it was found that the model on site was not suitably designed and maintained. The data parameters that was used for the on-site installed model was also not possible to be calculated when the model was simulated using Homer Software. As a result, we proposed a new model design by optimizing the load of VSAT and charging station based on the amount of solar PV and batteries that was supplied. This paper shows the significance of preliminary designs based on the irradiance and the usage load of the specific site before any installation should be commenced. It also showed how the site should be maintained properly in order to ensure a chosen site is sustainable for the rural community, post-installation.

Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

The precise estimation of solar radiation data is substantial in the long-term evaluation for the techno-economic performance of solar energy conversion systems (e.g., concentrated solar thermal collectors and photovoltaic plants) for each site around the world, particularly, direct normal irradiance which is utilized commonly in designing solar concentrated collectors. However, the lack of direct normal irradiance data comparing to global and diffuse horizontal irradiance data and the high cost of measurement equipment represent significant challenges for exploiting and managing solar energy. Consequently, this study was performed to develop two hierarchical methodologies by using various models, empirical correlations and regression equations to estimate hourly solar irradiance data for various worldwide locations (using new correlation coefficients) and different sky conditions (using cloud cover range). Additionally, the preliminary assessment for the potential of solar energy in the selected region was carried out by developing a comprehensive analysis for the solar irradiance data and the clearness index to make a proper decision for the capability of utilizing solar energy technologies. A case study for the San Antonio region in Texas was selected to demonstrate the accuracy of the proposed methodologies for estimating hourly direct normal irradiance and monthly average hourly direct normal irradiance data at this region. The estimated data show a good accuracy comparing with measured solar data by using locally adjusted coefficients and different statistical indicators. Furthermore, the obtained results show that the selected region is unequivocally amenable to harnessing solar energy as the prime source of energy by utilizing concentrating and non-concentrating solar energy systems.

Analysis and research on quality control method of global radiation observation data

Radiation observation data are important basic data for assessment and research of climate change and solar energy resources. Quality control of radiation observation data is an important guarantee for accurate and reliable observation. First, the global radiation data collection method and the quality control process are introduced, and the differences and similarities between different quality control standards are compared in detail. Then, the quality control contents of the upper and lower limits and the rate of change are analysed theoretically and verified by experiments. Theoretical analysis and experimental results showed that the lower limit of the global radiation irradiance quality control should be −20 W/m2, the upper limit should be 2221 W/m2 or based on solar position, and the wrong change rate of sampling value should be 1000 W/m2. Through the unification of the global radiation quality control standards, the difference caused by the inconsistent quality control methods can be avoided, thus improving the effectiveness of the radiation observation data and ensuring the accuracy and consistency of the radiation observation data.

Dependence of remote sensing accuracy of global horizontal irradiance at different scales on satellite sampling frequency

The emergence of new-generation geostationary satellites (such as Himawari-8 or FenYun-4) provides us an opportunity to obtain more accurate global horizontal irradiance (GHI) data. In this study, instantaneous GHI estimates (with a spatio-temporal resolution of 10 min and 5 km) are produced using the cloud products from the new-generation geostationary satellite Himawari-8 with a physically based algorithm. The hourly, daily, and monthly GHI estimates are aggregated using “snapshots” (or instantaneous estimates) at two different sampling frequencies (i.e., 1 h and 10 min), and validated against surface radiometry measurements collected in China. The root mean square errors (RMSE) for hourly, daily and monthly GHI estimates calculated using 1-h snapshots were 106.6, 27.9, and 17.7 W m−2, respectively, whereas those for hourly, daily and, monthly GHI calculated using 10-min snapshots decreased to 94.0, 24.0, and 16.6 W m−2, respectively. This result demonstrates that the accuracies of hourly, daily, and monthly GHI estimates are improved by increasing the frequency of satellite observations from 1 h (frequency of the previous-generation geostationary satellites) to 10 min (frequency of the new-generation geostationary satellites).

Solar Irradiation Data Processing using estimator MatriceS (SIMS) validated for Portugal (southern Europe)

Accurate solar resource assessment is essential in all the different phases of solar energy systems design and implementation. On a local scale, the solar resource is best assessed from ground measurements and, if available, with the existence of complete time-series of hourly values for the long-term resource estimation. However, these usually suffer from the occurrence of data gaps that can be as large as several months for remote and/or less maintained stations. Such gaps hinder the correct assessment of solar availability and, for that matter, their filling is a crucial first step to perform an appropriate assessment. In this context, a method for Solar Irradiation Data Processing using estimator MatriceS (SIMS) has been developed and is presented here. The algorithm allows to determine long-term linear correlations between a network of spatially distributed stations and, with these, to identify possible outliers and to fill data gaps through the selection of the median value from the obtained estimations. The method is validated against global horizontal irradiation (GHI) data from a network that comprises 89 ground-measuring stations, being maintained by the Portuguese Meteorology Service (IPMA - Instituto Português do Mar e da Atmosfera), considering a period of 17 years (from 2001 to 2017). Two important assertions are made for coefficients between stations: (1) coefficients only decrease slightly with the distance between stations (with a median reduction of ∼0.0003 km−1), for the considered network; (2) asymptotic long-term coefficients are reached with only one year of data. Taking advantage of the SIMS method, GHI assessment is presented here in the form of availability maps over the Portuguese mainland, with the respective values being listed in a table for future reference. The present assessment confirms that Portugal is a suitable region for the implementation of solar energy systems, with GHI having availabilities up to 2028.4 kWh/m2/year ±3.4% in Sagres (southernmost part of Portugal).

Solcast: Validation of a satellite-derived solar irradiance dataset

Solcast (https://solcast.com/) is a global solar forecasting and historical solar irradiance data company. Their datasets are important to the scientific community as substantial amounts of historical data is made freely available to researchers. Solcast’s hourly averages and sub-hourly instantaneous (10/15-min) global horizontal irradiance (GHI) estimates are validated at 48 BSRN ground stations that contain 1,014,792 valid hourly average measurements and 2,932,976 valid instantaneous measurements. The data are validated globally and across four climates under clear-sky, clouded-sky and all-sky periods, repeated for ‘all zenith angles’ (θ_z<85°) and high-sun zenith angles (θ_z<50°). A site-by-site validation is also performed for both hourly and instantaneous datasets. All validations and recommendations are impartial and unbiased.The hourly dataset validated similarly for both zenith angle scenarios, which is remarkable due to the added complexities at low-sun angles. In terms of rRMSE, the global performances of hourly GHI under clear-sky, clouded-sky and all-sky were 3.4%, 25.6% and 16.9%, respectively. With each ground station equally weighted, the overall bias was 0.33%, and standard deviation of biases at 1.7%. Solcast’s instantaneous GHI product validations were at times similar to the hourly validations though is naturally harder with increasing temporal resolution.The historical datasets from Solcast are found to be of good quality, thus, they are recommended for use by researchers. Identified areas for improvement herein will guide future developments of Solcast’s methodology.

New approach to estimate 5-min global solar irradiation data on tilted planes from horizontal measurement

This paper presents a new methodology to estimate global tilted irradiation in 5-min steps using only global irradiation on the horizontal plane. The methodology is based on a combination of the two well-known conventional Perrin Brichambaut and Liu and Jordan models. Tilted irradiation is of great importance for the design and short-term performance assessment of fixed-tilt flat-plate collectors and photovoltaic (PV) systems. Intermediate key parameters of the state of the sky, referred to here as cloudiness factors, are determined and introduced to transform isotropic models into an anisotropic model. The results of the proposed model for all sky conditions with regard to normalized root mean square error (nRMSE), relative percentage error (RPE), normalized mean absolute error (nMAE) and coefficient of correlation (R2) range from 4.7 to 6.41%, 5.5–5.9%, 3.07–4.73% and 0.97 to 0.99, respectively, which are very accurate results, especially for such a short time step. A comparison with the best conventional models and even artificial neural network (ANN) models described in the literature has confirmed that the developed model has smaller errors.

Quality control of diffuse solar radiation component with satellite-based estimation methods

In order to analyze solar systems, it is very important to determine the accurate solar radiation components belonging to any region. These components are defined as the beam and diffuse. The diffuse radiation component may not always be determined accurately since it is mostly affected by many geographical factors and climatic features in addition to the location parameters. Therefore, this work aims to compare and evaluate the validity of global and diffuse estimation methods for nine locations in Turkey. The satellite-based estimation methods (HELIOSAT, Meteonom, and PVGIS) mentioned in the literature were used to estimate global and diffuse radiation. These selected methods were statistically tested with accurate ground measured data. For HELIOSAT, the global radiation is varying −0.02 and 0.03, and 0.010 and 0.035, respectively, Relative MBE and Relative RMSE. Similarly, the diffuse radiation values are −0.020 and 0.025 for Relative MBE, and 0.015 and 0.030 for Relative RMSE. The goodness of the fit values for the HELIOSAT method shows a very good agreement with at least 97% confidence level of global and 91% of diffuse radiation values. Thus, HELIOSAT method shows a trustworthy alternative to the ground measurement data when reliable global and diffuse radiation data are not available.