Radiometric Stations Research Articles

Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

AbstractA methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. The random forest machine learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky camera images and 7 from the ceilometer. The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. of Jaén (Spain). Up to nine different types of clouds (plus clear sky) were considered (clear sky, cumulus, stratocumulus, nimbostratus, altocumulus, altostratus, stratus, cirrocumulus, cirrostratus, and cirrus) plus an additional category multicloud, aiming to account for the frequent cases in which the sky is covered by several cloud types. A total of eight experiments was conducted by (1) excluding/including the ceilometer information, (2) including/excluding the multicloud category, and (3) using six or nine different cloud types, aside from the clear‐sky and multicloud category. The method provided accuracies ranging from 45% to 78%, being highly dependent on the use of the ceilometer information. This information showed to be particularly relevant for accurately classifying “cumuliform” clouds and to account for the multicloud category. In this regard, the camera information alone was found to be not suitable to deal with this category. Finally, while the use of the ceilometer provided an overall superior performance, some limitations were found, mainly related to the classification of clouds with similar cloud base height and geometric thickness.

Numerical Simulation of Heat Exchanges for a Desert House Type ADRAR

The main objective of this work is to study the thermal exchanges in a habitable enclosure located in a desert region of Algeria (Adrar). This latter is considered as an air volume of parallelepiped shape limited by horizontal and vertical flat walls. The walls are the only capacitive elements of the enclosure. They are thermally coupled by convection and radiation and are the seat of conductive flux. The external facades of the enclosure are the seat of a convective flux with the external air and radiative exchanges with the environment (ground and sky). Openings (cracks, sealing defects, infiltration orifices, renewal orifices, etc.) allow the air to circulate inside the habitable enclosure and between the inside and the outside. Thermal exchanges are studied using the balance equations established at each wall of the enclosure. These equations have been discretized by an implicit finite difference method. The systems of algebraic equations thus obtained have been solved by the Gauss algorithm using the nodal method. The effects of the outdoor ambient temperature, the density of the incident solar flux on the facades and the orientation of the habitable enclosure in the meridian plane on the temperature distributions of the internal walls and the filled air in the enclosure havec been analyzed on the basis of recent climate data measured at the ADRAR Saharan Renewable Energy Research Unit. An analysis of the evolution of the internal ambient temperature as a function of the wind exposure factor of the heated space and of the degree of leaktightness of the doors and windows was also carried out at the end of this work. An acceptable agreement was found between the numerical results and those measured by the radiometric station. Moreover, the results obtained show that the building material used in this region causes undesirable overheating due to its thermal inertia.

A method for estimating direct normal irradiation from GOES geostationary satellite imagery: Validation and application over Northeast Brazil

Abstract The mapping and monitoring of Direct Normal Irradiance (DNI) is most relevant for the proper installation of solar power plants that use concentrating solar systems. This paper presents a satellite-based method for estimating DNI and daily direct normal irradiation (Qn) that uses a minimal set of regional meteorological information and avoids empirical adjustment with ground-based radiometric data. The focus of the validation and application were on the Northeast Brazil region (NEB), which exhibits high solar radiation levels throughout the year. Two basic parameters for satellite-derived DNI estimates are: (i) DNI under clear-sky conditions DNIclear (computed from the REST model) and (ii) cloud cover C (estimated from GOES visible imagery). The first validations were performed by comparison with measurements from three radiometric stations of the SONDA network in the NEB (Petrolina, Natal and Sao Luis) during 2007–2008. Overall, the results confirmed the good performance of the proposed model. DNI daily cycles in conditions of clear and partially cloudy skies were represented satisfactorily, with daily average errors within ±25 W m−2. Good linearity was found between the measured and satellite-estimated Qn, with coefficients of determination (R2) ranging from 0.84 to 0.89. The average values of MBE (Mean Bias Error) and RMSE (Root Mean Square Error) over all sites were −1.7% and 18.7%, respectively. In addition, seasonal and annual average maps of direct normal irradiation were generated over NEB for 2008. It was found that the spatial–temporal variability of Qn is strongly modulated by meteorological systems acting in the region, such as the ITCZ (Intertropical Convergence Zone). The mapped results reveal that the highest value was approximately 22 MJ m−2 on an annual average basis, and higher values occurred in areas of semi-arid climate. The proposed satellite-based model may be applied for providing information of DNI (with a spatial resolution of 4 km), particularly over regions without in situ measurements, and suggests good accuracy for climatic and solar resource studies.

Performance Evaluation and Comparison of Two Cascaded Configurations of PV Generators-Five Levels Inverter for a Stand-Alone Application in South Algeria

In this paper two configurations of solar photovoltaic energy conversion using the NPC five levels inverter for stand-alone application in south Algeria are proposed and their performances compared. The first cascade uses four separate PV sources and the second configuration use only one PV generator. In these two cases and without DC/DC converter introduced between PV source and inverter and to get a stable AC voltage, authors in propose a proportional regulator of inverter modulation index. The SVPWM technique is used in order to get the best voltage waveform. For the second configuration proposed, we introduce in the control loops another algorithm which uses the redundant vectors of space vector diagram of inverter to stabilise the DC bus voltages. A real data of temperature and solar irradiation obtained by radiometric station in Ghardaïa city in south Algeria are used to test the performance of proposed controls. The simulation results show that the inverter output voltage is stable for the two configurations proposed despite the variation of solar irradiation, temperature and load. Also, the THD obtained is in the limits of international standards. Then, the PV cascade with separate PV sources is the best solution, seeing that we do not need to use another algorithm in the control loops.

A CRO-species optimization scheme for robust global solar radiation statistical downscaling

This paper tackles the prediction of the global solar radiation (GSR) at a given point, using as predictive variables the outputs of a numerical weather model (the WRF meso-scale model) obtained at a different grid points. Prediction is obtained in this work using a Multilayer Perceptron (MLP) trained with Extreme Learning Machines (ELMs). Provided that the number of WRF outputs is vast, we propose the use of a Coral Reefs Optimization algorithm with species (CRO-SP) to obtain a reduced number of significant predictive variables, therefore improving the global solar radiation prediction attained without feature selection. The proposed system has been tested on real data from a radiometric station located at Toledo (Spain) and average best results of RMSE of 69.19 W/m2 have been achieved, resulting in a 21.62% improvement over the average prediction without considering the CRO-SP for the feature selection.

Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks

This paper presents three different topologies of feed forward neural network (FFNN) models for generating global, direct, and diffuse hourly solar irradiance in the city of Fez (Morocco). Results from this analysis are crucial for the conception of any solar energy system. Especially, for the concentrating ones, as direct component is seldom measured. For the three models, the main input was the daily global irradiation with other radiometric and meteorological parameters. Three years of hourly data were available for this study. For each solar component’s prediction, different combinations of inputs as well as different numbers of hidden neurons were considered. To evaluate these models, the regression coefficient (R2) and normalized root mean square error (nRMSE) were used. The test of these models over unseen data showed a good accuracy and proved their generalization capability (nRMSE = 13.1%, 9.5%, and 8.05% and R = 0.98, 0.98, and 0.99) for hourly global, hourly direct, and daily direct radiation, respectively. Different comparison analyses confirmed that (FFNN) models surpass other methods of estimation. As such, the proposed models showed a good ability to generate different solar components from daily global radiation which is registered in most radiometric stations.

Modeling the Global Solar Radiation Under Cloudy Sky Using Meteosat Second Generation High Resolution Visible Raw Data

This paper presents an improved model for mapping both hourly and daily values of the global radiation reaching the earth’s surface from satellite data over Algeria. An empirical model with a large spectral band using Linke atmospheric turbidity was chosen to modeling the clear-sky global radiation. The extinction of the solar radiation (absorption plus scattering) is described by an exponential decay function of the cloud albedo and cloud index. To compute these two parameter, we resolve the sun-pixel-satellite radiative model equation using the following hypothesizes. The first one supposes the total transmittance of the atmospheric in the pixel-satellite direction as the ratio of the direct radiation in real sky to the same quantity in case of a very clean sky. The second one estimates that the noise energy reflected back towards the radiometer by molecules of gases in the upper levels of the atmosphere represents about 5 % of the energy measured by the radiometer. The model performance was validated for five radiometric stations across the country for three selected months in 2013 and satisfactory results were obtained and discussed.

Assessing the Surface Solar Radiation Budget in the WRF Model: A Spatiotemporal Analysis of the Bias and Its Causes

Abstract Solar radiation plays a key role in the atmospheric system but its distribution throughout the atmosphere and at the surface is still very uncertain in atmospheric models, and further assessment is required. In this study, the shortwave downward total solar radiation flux (SWD) predicted by the Weather Research and Forecasting (WRF) Model at the surface is validated over Spain for a 10-yr period based on observations of a network of 52 radiometric stations. In addition to the traditional pointwise validation of modeled data, an original spatially continuous evaluation of the SWD bias is also conducted using a principal component analysis. Overall, WRF overestimates the mean observed SWD by 28.9 W m−2, while the bias of ERA-Interim, which provides initial and boundary conditions to WRF, is only 15.0 W m−2. An important part of the WRF SWD bias seems to be related to a very low cumulus cloud amount in the model and, possibly, a misrepresentation of the radiative impact of this type of cloud.

A theoretical framework for Ångström equation. Its virtues and liabilities in solar energy estimation

The relation between solar irradiation and sunshine duration was investigated from the very beginning of solar radiation measurements. Many studies were devoted to this topic aiming to include the complex influence of clouds on solar irradiation into equations. This study is focused on the linear relationship between the clear sky index and the relative sunshine proposed by the pioneering work of Ångström. A full semi-empirical derivation of the equation, highlighting its virtues and liabilities, is presented. Specific Ångström – type equations for beam and diffuse solar irradiation were derived separately. The sum of the two components recovers the traditional form of the Ångström equation. The physical meaning of the Ångström parameter, as the average of the clouds transmittance, emerges naturally. The theoretical results on the Ångström equation performance are well supported by the tests against measured data. Using long-term records of global solar irradiation and sunshine duration from thirteen European radiometric stations, the influence of the Ångström constraint (slope equals one minus intercept) on the accuracy of the estimates is analyzed. Another focus is on the assessment of the degradation of the equation calibration. The temporal variability in cloud transmittance (both long-term trend and fluctuations) is a major source of uncertainty for Ångström equation estimates.

An adaptive multi-modeling approach to solar nowcasting

The ability to forecast solar irradiance in near-real time (nowcasting) is crucial in managing the integration of solar energy in power grids. This paper focuses on minute-by-minute forecasts of the normalized clearness index, a measure of global horizontal irradiation, within a fifteen steps-ahead temporal horizon, using data collected with a radiometric station in Doha, Qatar, for the period January–December 2014. We describe a novel multi-modeling approach to solar forecasting that uses supervised classification of forecasting evaluation results from diverse stochastic models to select the best predictions, according to their expected superiority in terms of lower error rate. The hypothesis that such a multi-modeling approach rivals the performance of any single forecasting model is tested with reference to two autoregressive models, of order 3 and 11 respectively, a support vector regression model, and a persistence model which provide the baseline for solar prediction. The advantages of the proposed approach are demonstrated in an experimental evaluation where its application with these four models shows a relative skill score improvement of 44.92% over the baseline model, and 19.06% over the best performing model (autoregressive of order 11).

Comparison of solar radiation models and their validation under Algerian climate – The case of direct irradiance

The accurate prediction of direct solar irradiance is essential in many solar energy applications, particularly those relying on concentrating solar technologies. The present paper is aimed at a detailed assessment of a large range of clear-sky solar radiation models under Algerian climate to select the more accurate one for estimating the performance of solar power projects where meteorological and radiometric measurement stations are not available. To this end, seventeen models have been reviewed and their performance compared to measured irradiance of Ghardaia (Southern Algeria). The validation methodology presented herein is very helpful for ranking the models. A new statistical accuracy indicator has been originally introduced to find out the most accurate ones.A thorough analysis of selected models has shown that the more complex models, that seem at first sight more sophisticated, are not necessary the most accurate; while simpler models depending on a limited number of parameters are more suitable. In other words, the suitability and accuracy of a model do not necessarily improve with an increase in the number of its parameters. This important finding is in good agreement with the previous published studies. This fact is important to take into account, in the case where measured data are not available, for the selection of the most suitable locations for the installation of the future concentrating solar power plants in Algeria or even in other countries.

An advanced ANN-based method to estimate hourly solar radiation from multi-spectral MSG imagery

In this work, a new method to derive hourly global horizontal irradiance (GHI) estimates from Meteosat Second Generation (MSG) imagery is presented. The method is based on an optimized Artificial Neural Network (ANN) ensemble model using a selection of the best ANN models identified from an initial ensemble that discerns between different sky conditions and an additional ensemble that considers all sky conditions together. For benchmarking purposes, hourly GHI estimates computed with the Heliosat-2 method, accounting for the diurnal variability of ground albedo, are used. Data collected during the 3-year period from 2009 to 2011 at 28 radiometric stations located in northern Africa, Middle East and Europe, are used in the procedure. From these stations, 7 are used to train the ANN models and the other 21 for independent validation. Results obtained with the proposed ANN ensemble model reduced the RMSE value of the Heliosat-2 model a 22% for all-sky conditions and a 42% for overcast conditions.

Optimal combination of gridded and ground-observed solar radiation data for regional solar resource assessment

Proper quantification of the available solar resource is essential for the diverse development phases of any solar power plant. At local scale, the solar resource is best assessed from ground radiometric stations. At regional-to-continental scales, however, satellite-based techniques are currently the most suitable approach. Solar radiation evaluation using numerical weather prediction (NWP) models presents some advantages over satellite-based techniques. Nonetheless, gridded solar radiation estimates using either satellite-based or NWP-based techniques still produce biased estimates which are often much higher for the latter. Therefore, a correction needs to be applied before these gridded values are usable for solar applications, especially for NWP-based estimates. This contribution introduces an original method based on the optimal interpolation technique to adjust gridded solar radiation estimates consistently with concomitant radiometric ground observations. The method’s performance is demonstrated using NWP-based gridded estimates with 10-km spacing of global and direct monthly irradiation data during the 10-year period from 2003 to 2012 over continental Spain and the Balearic Islands. It is shown that the proposed methodology produces adjusted gridded values that are (or nearly always are, in the case of direct irradiation) within the expected measurement uncertainty of the ground observations, provided a sufficiently large number of observations is available for correction. For the studied region and these NWP-based gridded datasets, our findings suggest that a homogeneous mean distance between ground observations of 100–150km can result in unbiased gridded estimates.

Reconstruction of long-term direct solar irradiance data series using a model based on the Cloud Modification Factor

This paper develops a semi-empirical model to calculate direct solar shortwave irradiance on a horizontal surface for any cloudiness condition. The model is based on radiative transfer simulations combined with empirical relationships; it makes use of the global solar irradiance, experimentally measured in most of the radiometric stations, and the Cloud Modification Factor (CMF). A dataset of irradiance from eight Spanish locations is used to tune the model parameters and a part of the data is used to validate the model. By using long-term series of experimental global irradiance data, the corresponding long-term direct irradiances are reconstructed for each location using the proposed model. An evaluation of the model performance is carried out for different solar zenith angles. Following, monthly averages of the hourly data were calculated from the reconstructed values. A high correlation is found when compared with the corresponding measured values. To make use of the obtained results, a characterization of the typical climatic values of the solar direct irradiance in Spain is carried out. The results provide representative values of solar direct radiation and show the availability of this solar resource in the studied areas.

Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes

Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used.

Simultaneous Stochastic Simulation of Monthly Mean Daily Global Solar Radiation and Sunshine Duration Hours Using Copulas

AbstractIn this paper, copula functions are used to model the dependence structure of monthly mean solar radiation R and sunshine duration hours D. The efficiency of five well-known bivariate parametric copula functions, including (1) normal, (2) student’s t, (3) Clayton, (4) Frank, and (5) Gumbel, are evaluated in a seasonal basis for nine radiometric stations in Iran. First, the most appropriate marginal probability distributions for R and D were individually selected from 16 univariate distributions; then, performance of the parametric copulas for modeling the dependence structure of R-D joint empirical probability distribution was assessed using two criteria. Finally, based on appropriate parametric copulas, the joint simulation of marginal variables was accomplished using the conditional sampling technique. The results show that the best marginal distribution fitted on the original data D and R is normal distribution when transformed with Johnson function (in more than a half of cases). Because of hi...

A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model

Abstract. Broadband short-wave (SW) surface direct and diffuse irradiances are not typically within the set of output variables produced by numerical weather prediction (NWP) models. However, they are frequently requested for solar energy applications. In order to compute them, a detailed representation of the aerosol optical properties is important. Nonetheless, NWP models typically oversimplify aerosol representation or even neglect their effect. In this work, a flexible method to account for the SW aerosol optical properties in the computation of broadband SW surface direct and diffuse irradiances is presented. It only requires aerosol optical depth at 0.55 μm and knowledge of the type of predominant aerosol. Other parameters needed to consider spectral aerosol extinction, namely, Angström exponent, aerosol single-scattering albedo and aerosol asymmetry factor, are parameterized. The parameterization has been tested using the Rapid Radiative Transfer Model for climate and weather models (RRTMG) SW scheme of the Weather Research and Forecasting (WRF) NWP model for data over the continental US. In principle, it can be adapted to any other SW radiative transfer band model. It has been verified against a control experiment and using data from five radiometric stations in the contiguous US. The control experiment consisted of a clear-sky evaluation of the RRTMG solar radiation estimates obtained in WRF when RRTMG is driven with ground-observed aerosol optical properties. Overall, the verification has shown satisfactory results for both broadband SW surface direct and diffuse irradiances. The parameterization has proven effective in significantly reducing the prediction error and constraining the seasonal bias in clear-sky conditions to within the typical observational error expected in well maintained radiometers.

Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach

This paper discusses the performance of a novel Coral Reefs Optimization – Extreme Learning Machine (CRO–ELM) algorithm in a real problem of global solar radiation prediction. The work considers different meteorological data from the radiometric station at Murcia (southern Spain), both from measurements, radiosondes and meteorological models, and fully describes the hybrid CRO–ELM to solve the prediction of the daily global solar radiation from these data. The algorithm is designed in such a way that the ELM solves the prediction problem, whereas the CRO evolves the weights of the neural network, in order to improve the solutions obtained. The experiments carried out have shown that the CRO–ELM approach is able to obtain an accurate prediction of the daily global radiation, better than the classical ELM, and the Support Vector Regression algorithm.

Estimating aerosol characteristics from solar irradiance measurements at an urban location in southeastern Spain

Under cloudless conditions aerosols are the main atmospheric components responsible for direct effects on solar radiation. Measurements of aerosol optical properties along with simultaneous measurements of solar irradiances (global –G– and diffuse –D–) were recorded at an urban site (Granada, Spain) to characterize the radiative effect of atmospheric aerosols from 2006 to 2008. The selection of cloudless conditions was made considering cases with 0 oktas. To avoid cloud contamination, a restricted data set with clearness index larger than 0.65 and maximum D of 200 W/m2 was used. The analysis was performed evaluating G, D, and IN (direct normal irradiance) and the ratios between them in association with aerosol optical depth (AOD) at 675 nm. Results show an aerosol forcing efficiency of −279 ± 21 W/m2 per unit of AOD at 675 nm at 15° solar zenith angle and a maximum value in June for monthly mean aerosol radiative forcing of −23 ± 7 W/m2. Large dependency was shown of the ratios D/G and D/IN which increased with increasing AOD, while IN/G decreased. On the other hand, the ratio D/IN was the most reliable parameter to estimate AOD with a coefficient of determination of 0.94; the empirical relationship obtained was validated using an independent data set obtaining 2.5% mean bias deviation and 13.5% root-mean-square deviation. This relationship constitutes an alternative tool for estimating AOD from routine irradiance measurements available from numerous radiometric stations worldwide.

On the determination of coherent solar microclimates for utility planning and operations

This work presents a cluster analysis for the determination of coherent zones of Global Horizontal Irradiance (GHI) for a utility scale territory in California, which is serviced by San Diego Gas & Electric. Knowledge of these coherent zones, or clusters, would allow utilities and power plants to realize cost savings through regional planning and operation activities such as the mitigation of solar power variability through the intelligent placement of solar farms and the optimal placement of radiometric stations. In order to determine such clusters, two years of gridded satellite data were used to describe the evolution of GHI over a portion of Southern California. Step changes of the average daily clear-sky index at each location are used to characterize the fluctuation of GHI. The k-means clustering algorithm is applied in conjunction with a stable initialization method to diminish its dependency to random initial conditions. Two validity indices are then used to define the quality of the cluster partitions as well as the appropriate number of clusters. The clustering algorithm determined an optimal number of 14 coherent spatial clusters of similar GHI variability as the most appropriate segmentation of the service territory map. In addition, 14 cluster centers are selected whose radiometric observations may serve as a proxy for the rest of the cluster. A correlation analysis, within and between the proposed clusters, based both on single-point ground-based and satellite-derived measurements evaluates positively the coherence of the conducted clustering. This method could easily be applied to any other utility scale region and is not dependent on GHI data which shows promise for the application of such clustering methods to load data and/or other renewable resources such as wind.