Radiometric Stations Research Articles

Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

AbstractThe short-term variability of solar resource is one of the main challenges faced by the large-scale implementation of photovoltaic (PV) systems today; this will increase as the installed power of solar PV increases. To evaluate its influence on electrical grid operation, it is essential to analyse the variability of solar resource based on high-frequency data, 30 s and shorter. In this study, the high-frequency measurements of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) recorded every 5 s at the radiometric station of the University of Seville for 20 years are analysed and characterised. For this purpose, a corrected database for GHI and DNI was obtained from the application of a correction methodology. To understand the reliability of this database it is required to know its uncertainty. This work proposes a methodology to quantify the uncertainty of high-frequency radiometric databases whose wrong data have been corrected. Applying it, the daily uncertainty is 2.69% for GHI and 4.07% for DNI. To characterize the distribution of the database, the distribution of high frequency kt and kb indices are modelled providing the fitting parameters. To characterize the variability of high-frequency measurements, transition matrices are proposed, which allow identifying both the magnitude and frequency of jumps. The results obtained show that jumps of up to 800 W/m2 occur in both GHI and DNI. In the case of GHI, the percentage of jumps equal to or greater than 500 W/m2 is 1.36%, about 43,000 jumps in a year in measurements every 5 s.

Statistical Approach to the Synergy of Data from Caliop and Ground-Based Radiometric Stations for Studying Altitude Profiles of Aerosol Parameters

Statistical Approach to the Synergy of Data from Caliop and Ground-Based Radiometric Stations for Studying Altitude Profiles of Aerosol Parameters

Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain

Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics.

The solar radiation evaluation by satellite images processing

. To evaluate the global solar irradiation flux received at the ground surface for Algeria, an analytical model is implemented by processing satellite images and solving the equation of radiative transfer. This model is derived from that initially worked out by C. Gautier et al. in 1980 using high-resolution Goes images. We get that it is well adapted to the processing of lesser resolution images such as those collected by Meteosat 2 following the B2 format. The data base under study mainly consists of clear-sky B2 Meteosat images recorded every three hours in the visible channel (i.e., [0.4 – 1.1 µm]) during the 1986/87 period and representing North Africa and Southern Europe. Hourly and daily global solar irradiation fluxes received at the ground on a horizontal surface have therefore been evaluated by applying the analytical model to the Meteosat images. The obtained results were compared to the hourly ground solar measurements recorded in the radiometric stations of Bouzareah (Algiers) and Oran during the 1986/87 period. Since the deviations between both types of solar data do not exceed 10%, the radiances estimated by modelling are found to be close to the related ground measurements.

Improving the Estimation of the Diffuse Component of Photosynthetically Active Radiation (PAR)

AbstractMost weather forecasting models are not able to accurately reproduce the great variability existing in the measurements of the diffuse component of photosynthetically active radiation (PAR; 400–700 nm) under all sky conditions. Based on the well‐known relationship between the diffuse fraction (k) and the clearness index (kt), this study addresses improvements in estimations by proposing adaptations of previous models, which were previously applied only to the total solar irradiance (TSI; 280–3,000 nm). In order to reproduce this variability, additional parameters were introduced. The models were tested employing a multisite database gathered at the Mediterranean basin. Since Artificial Neural Network (ANN) models are not limited to fixed coefficients to predict the diffuse fraction of PAR (kPAR), these types of models are more accurate than empirical ones, reaching determination coefficients (r2) up to 0.998. However, the simpler linear model proposed by Foyo‐Moreno et al. (2018), https://doi.org/10.1016/j.atmosres.2017.12.012 shows a similar performance to the ANN models, directly predicting the diffuse component of PAR (PARDiffuse) from TSIDiffuse, with a r2 up to 0.997. Results obtained here also determine that the most important variables for estimating PARDiffuse are kt or kt,PAR, and the apparent solar time (AST). Therefore, PARDiffuse can be modeled using TSI measured in most radiometric stations, reaching r2 up to 0.858 for empirical models and 0.970 for ANN models. This modified approach will allow for the very accurate construction of long‐term data series of PARDiffuse in regions where continuous measurements of PAR are not available.

Evaluation of the global solar irradiation from the artificial neural network technique

In this study, many experiments were carried out to assess the influence of the some input parameters on performance of the multilayer perceptron techniques, which is architectures one of the artificial neural network (ANN). To estimate the daily global solar irradiation (GSI) on horizontal surface, we have developed eight models by using four weather input parameters collected from a radiometric station installed at Ghardaia site (southern of Algeria), such as sunshine duration, daily mean temperature, daily relative humidity, and Solar declination. In order to select the best configuration among the chooses combinations which provides a good accuracy, three statistical formulas (or statistical indicators) have been evaluated, such as the normalized root mean square errors, (nRMSE), normalized mean bias error (nMBE, and coefficient of determination R²). We noted that the ANN model provides best performance compared to the developed empirical models. results of the nMBE, nRMSE, and R² are about 0.048%, 4.422%, and 98.00%, for ANN and 0.0772%, 5.5381%, and 97.22% for the quadratic model. Moreover, it was proved that the sunshine duration is important parameter, for predicting the GSI.

SPARTA: Solar parameterization for the radiative transfer of the cloudless atmosphere

The high-performance SPARTA broadband clear-sky solar irradiance model is presented. It evaluates global (GHI), diffuse (DIF) and direct normal (DNI) irradiances from atmospheric transmittance functions developed using an advanced 2-band hybrid spectral integration scheme, calculates aerosol extinction using a universal and highly accurate aerosol transmittance scheme, and incorporates a versatile parameterization of the aerosol circumsolar solar irradiance. The model's performance is assessed against 1-min quality-assured measurements at three research-class radiometric ground stations spanning 15 years of data combined, and it is benchmarked against three high-performance radiative transfer models. The performance assessment proved that SPARTA was superior to the reference models for GHI, DIF and DNI at the three observational sites combined. SPARTA produced unbiased estimates of the three solar irradiance components (remarkably, it was the only model producing unbiased estimates of DIF) and yielded the smallest standard deviations of the model−observation differences (≈2 %, ≈10 % and ≈2 % for GHI, DIF and DNI, respectively). SPARTA was the only model capable of improving the estimation of DIF when using observed inputs of the aerosol scattering optical properties instead of modelled values. The results of the performance assessment proved that, provided accurate inputs to the model, SPARTA produces predictions with similar uncertainty than ground observations, especially if the ground sensors are not optimaly maintained or they are not A or B ISO-9060 class.

Prediction of direct normal irradiation using a new empirical sunshine duration-based model

In this work, we are interested in presenting a new approach allowing us to express the Direct Normal solar Irradiation (DNI) according to the sunshine duration essentially. This choice is justified by the fact that in addition to the sunshine, duration has a strong correlation with solar irradiation, it is measured in many radiometric stations. Some clear sky models with modifications developed exclusively here are made valid for all types of sky. The proposed model is compared with one of the intelligent models such as the Support Vector Regression (SVR) for daily data from Ghardaïa.

CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds

Precise sky classification as a function of cloudiness is desirable or necessary in a variety of applications. CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented here. It uses global horizontal irradiance (GHI) measurements at 1-min resolution, from which a set of four indices is derived to characterize the magnitude and temporal variability of GHI. The algorithm also requires precise estimates of 1-min GHI under hypothetical cloudless conditions, and the solar zenith angle (limited to a maximum of 85°). Using 1-min GHI measurements from 54 BSRN high-quality radiometric stations, which cover all five primary Köppen-Geiger climate classes, CAELUS is used here to classify their sky conditions. The classification results, including the distribution of sky classes and the transitions between consecutive sky classes, are found consistent with the known characteristics of each primary Köppen-Geiger climate. Moreover, in each climate class, the detection of 1-min cloudless situations is found comparable to that provided by two dedicated and state-of-the-art methods—Reno-Hansen and Bright-Sun.

A new method to estimate aerosol radiative forcing on photosynthetically active radiation

A new method to estimate aerosol radiative forcing (ARF) on photosynthetically active radiation (PAR) is proposed using as input only the solar position and global irradiance measurements, available in many radiometric stations worldwide. The main contribution of this work is the proposal of a new and simple tool (max-kt method) based in the parameterization of the envelope of the relationship between clearness index (kt) and the solar position. To this aim, a 1-year database (2020) of cloud-free data acquired in a Southwest Mediterranean site was used for the proposal and two more years (2017 and 2018) were used to extend the results. The ARF values retrieved using the new method were compared with estimates calculated by two physical models widely employed in the literature, such as the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer model) and an improved version of the CPCR2 (Code for Physical Computation of Radiation, 2 bands) model. The new method provided ARF values of the order of those provided by these physical models, especially with the SBDART model, confirming the validity of this new method. An ARF seasonal pattern was found with higher values in summer, (−30,7 ± 9,0) Wm−2 in August during 2017, (−40,1 ± 11,8) Wm−2 in June during 2018 and (−28,8 ± 7,7) Wm−2 in July during 2020 and minimum values in winter, (−8,1 ± 5,1) Wm−2 in January during 2017, (−5,7 ± 5,8) Wm−2 in December during 2018 and (−7,8 ± 5,4) Wm−2 in December during 2020. Moreover, a dependence on solar zenith angle (θz) was detected excepting during the year 2018, increasing ARF absolute values at θz from 0° to 45°-60° and decreasing to zero for the Sun near the horizon. This technique is very useful due to the difficulty of knowing all the inputs requested by the physical models.

Increasing the Resolution and Spectral Range of Measured Direct Irradiance Spectra for PV Applications

The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only sense the solar spectrum within a wavelength range that is usually too short compared to the actual spectral response of some PV technologies. In this work, a new methodology based on the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) spectral code is proposed to extend the spectral range of measured direct irradiance spectra and to increase the spectral resolution of such experimental measurements. Satisfactory results were obtained for both clear and hazy sky conditions at a radiometric station in southern Spain. This approach constitutes the starting point of a general methodology to obtain the instantaneous spectral irradiance incident on the plane of array of PV modules and its temporal variations, while evaluating the magnitude and variability of the abundance of atmospheric constituents with the most impact on surface irradiance, most particularly aerosols and water vapor.

Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models

Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa’s radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model’s predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model.

Spatio-Temporal Forecasting of Global Horizontal Irradiance Using Bayesian Inference

Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis. It is crucial to explore multisite modeling to capture variations in weather conditions between various sites, thereby producing a more robust model. In this research, we propose the use of spatial regression coupled with Gaussian Process Regression (GP Spatial) and the GP Autoregressive Spatial model (GP-AR Spatial) for the prediction of GHI using data from seven radiometric stations from South Africa and one from Namibia. The results of the proposed methods were compared with a benchmark model, the Linear Spatial Temporal Regression (LSTR) model. Five validation sets each comprised of three stations were chosen. For each validation set, the remaining five stations were used for training. Based on root mean square error, the GP model gave the most accurate forecasts across the validation sets. These results were confirmed by the statistical significance tests using the Giacommini–White test. In terms of coverage probability, there was a 100% coverage on three validation sets and the other two had 97% and 99%. The GP model dominated the other two models. One of the study’s contributions is using standardized forecasts and including a nonlinear trend covariate, which improved the accuracy of the forecasts. The forecasts were combined using a monotone composite quantile regression neural network and a quantile generalized additive model. This modeling framework could be useful to power utility companies in making informed decisions when planning power grid management, including large-scale solar power integration onto the power grid.

Extremal Dependence Modelling of Global Horizontal Irradiance with Temperature and Humidity: An Application Using South African Data

The interaction between global horizontal irradiance (GHI) and temperature helps determine the maximum amount of solar power generated. As temperature increases, GHI increases up to the point that it increases at a decreasing rate and then decreases. Therefore, system operators need to know the maximum possible solar power which can be generated. Using the multivariate adaptive regression splines, extreme value theory and copula models, the present paper seeks to determine the maximum temperature that will result in the generation of the maximum GHI ceteris paribus. The paper also discusses extremal dependence modelling of GHI with temperature and relative humidity (RH) at one radiometric station using South African data from 16 November 2015 to 16 November 2021. Empirical results show that the marginal increases of GHI converge to 0.12 W/m2 when temperature converges to 44.26 °C and the marginal increases of GHI converge to −0.1 W/m2 when RH converges to 103.26%. Conditioning on GHI, the study found that temperature and RH variables have a negative extremal dependence on large values of GHI. Due to the nonlinearity and different structure of the dependence on GHI against temperature and RH, unlike previous literature, we use three Archimedean copula functions: Clayton, Frank and Gumbel, to model the dependence structure. The modelling approach discussed in this paper could be useful to system operators in power utilities who must optimally integrate highly intermittent renewable energies on the grid.

Comparison of Satellite-Based and Ångström–Prescott Estimated Global Horizontal Irradiance under Different Cloud Cover Conditions in South African Locations

The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms.

Modified temperature-based global solar radiation models for estimation in regions with scarce experimental data

Reliable data on the availability of solar energy is needed, as solar energy is an essential resource for sustainable development worldwide. However, ground-based radiometric stations are scarce, at least in large areas far from population and in developing countries, so there are difficulties in validating methods for estimating solar radiation. Indirect models mitigate the problem by providing radiation data from other meteorological variables, which can be measured with low-cost equipment and calibrated with data from secondary station networks. However, models’ accuracy decreases if estimations are required far away from the calibration stations. It is hoped that modified models that include the influence of geographical and topographical variables can attenuate this drawback in data-scarce regions. This paper evaluates the accuracy and generality of 14 existing models of monthly global solar radiation based on temperature, which is a routinely measured variable. At first, models are locally calibrated at 105 stations in three large areas in Spain. Then, from the local coefficients of eight stations selected in each area, general equations are derived for the coefficients of each model as function of the ratio between elevation and distance to the sea. The predictions of these modified models, i.e., using coefficients derived from general equations, are compared both for the eight base stations and the remaining ones used for validation. In the comparisons, not only errors averaged in groups of stations are considered, but also local results. Several models perform well in some areas, but a simple homogeneous model is the only one whose indicators are good in all areas and hardly vary when using general coefficients derived from the data measured at all available stations.

The Implementation of Numerical Codes for the Analysis of Solar Flux Inputs and the Optimization of Thermal Comfort for a Monobloc Habitat

The purpose of this work is to design computer codes to estimate the different thermal exchanges of the facades of a habitable envelope with its environment in order to optimize thermal comfort. This optimization is based on the use of real climate data from the region under consideration. To achieve this goal, we have developed five fundamental codes in FORTRAN language. The first code consists in Modelling the flow of the heat transfer fluid in the heating slab pipe. The second is designed to model the heat transfer by conduction within the concrete slab. The third is developed for the Modelling of thermal exchanges in a habitable envelope assimilated to a parallelepipedal cavity based on the nodal method. The fourth code is reserved for the Modelling of solar radiation by evaluating the wage flux density on different positions of the walls. The fifth and last code is dedicated to the evaluation of the perfect thermal coupling between the concrete slab and the heat transfer fluid pipes. The validation of the models implemented in the calculation codes was made on the basis of data measured recently for a clear sky of solar radiation at the radiometric station of the renewable energies research unit in the Saharan environment URER’MS of Adrar. The results obtained showed a very good agreement between the calculated values using the computational codes developed and those measured by the radiometric station of the URER’MS during the typical day.

Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques

Satellite and reanalysis-derived solar products have gained great attention due to the inadequate number of radiometric stations worldwide, however, they are associated with considerable uncertainties. This study deals with the ground-based validation of Global Horizontal Irradiance from CAMS radiation service (GHICAMS) and the application of supervised machine learning algorithms (MLAs) to site-adapt GHICAMS. The validation of GHICAMS against measurements shows significant systematic and dispersion errors for all-sky (nMBE = 4.9% and nRMSE = 15.7%) and cloudy conditions (nMBE = 17.6% and nRMSE = 38.8%). Under clear skies, CAMS performs adequately (nMBE <1% and nRMSE <5%). All MLAs lead to reduced errors for the site-adapted irradiances. MBE is improved by more than 50%, accompanied by significant reductions in RMSE for various solar zenith angles and cloud fractions. The best results are revealed for the tree-based MLAs and especially for Random Forests.

Development of a Low-Cost Device for Measuring Ultraviolet Solar Radiation

Accurate measurements of solar ultraviolet radiation are needed for air quality monitoring, especially to understand the formation and photolysis of tropospheric ozone near the ground. It is interesting to investigate whether this demand could be met using recently developed low-cost UV sensors and new communication technologies. Despite the promising possibilities, their use is still scarce and their potential applications have not yet been thoroughly explored. This study aims to use low-cost sensors to develop devices that accurately measure solar ultraviolet radiation. The de vices should be low-cost, small, portable, and have low power consumption and IoT connectivity. For this purpose, three popular low-cost commercial sensors ML8511, UVM30A and VEML6075 are selected and implemented in several prototypes. The sensors are analyzed in terms of their spectral response, leveling, angular response and comparison with reference data. For that aim, experimental measurements are performed at the radiometric station of the Physics Department of the University of Extremadura in Badajoz, Spain. Results indicate that sensors of the same model might have different calibrations. The leveling and the angular response measurements indicate a strong azimuth dependence for the ML8511 and, especially, VEML6075 sensors, while the UVM30A sensor shows a much weaker dependence, probably due to the use of a circular diffuser. The angular response is identified as the main issue of the sensors, notably limiting their accuracy and preventing a widespread use. With the knowledge gained, a final version with LoRa communication and optimized power consumption is developed. The strength of the LoRa connection is measured at different locations on the University Campus using Receiver Signal Strength Indication. It ranges from -27 dB near the gateway to -122 dB at the farthest location on the Campus. The optimization of the power consumption allows 14 days of autonomy if operating only during daylight hours. The study illustrates the suitability of low-cost sensors for UV applications, provided that a good angular response of the sensors is ensured. It contributes to a wider use of these sensors for the measurement of air quality variables by identifying those characteristics that need to be improved by manufacturers to meet the standards.

Comparative Study of Experimentally Measured and Calculated Solar Radiations for Two Sites in Algeria

This paper presents a comparison study between the measured solar radiations on site and the calculated solar radiation based on the most theoretical models presented in the literature up to date. Indeed, for such purposes, this paper focusses on the analysis of the data of the measured solar radiation collected on two sites in Algeria such as Tlemcen (34°52′58″ N 01°19′00″ W, elevation 842 m) and Senia (35°39′ N 0°38′ W, elevation: 77 m). In order to check the accuracy of the proposed model, the experimental collected data of the solar radiation obtained from the existing radiometric stations installed at the two locations under investigation, are compared with the estimated or predicted solar radiations obtained from the Capderou and R.Sun models, where four days under clear skies are selected from different seasons to achieve this comparison. Second, the daily averages of the experimental global solar irradiation are compared to those predicted by Mefti model for both the sites. Finally, a validation is carried out based on the obtained experimental monthly global irradiations and with those estimated by Coppolino and Sivkov models. A relative difference is used in this case to judge the reliability and the accuracy of each model for both sites.