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The focus of this study is to develop a highly accurate formulation to estimate the day number (DN), solar declination angle (SOLDEC), solar altitude angle (SOLALT) and also to predict the diffuse horizontal irradiance (DHI), direct normal irradiance (DNI) and global horizontal irradiance (GHI) for any location around the world at any time of the day for both short term and long term periods. Regression analysis is done using continuous 12 years of satellite measured historical solar irradiance, weather and solar angle data in the temporal resolution of 10 min for 12 cities around the world such as Kuala Lumpur, Auckland, Tokyo, Riyadh, London, Accra, Antananarivo, Brasilia, Lima, Quito, Ottawa and Honolulu. The models generated through the regression analysis perform better than existing models in predicting the solar irradiance, hence, these models are efficient and reliable for universal global application.

The current study treats the prediction of the direct normal and the global horizontal irradiances (DNI and GHI) for five years measurements in Oujda city, Morocco. The prediction is performed by t...

An algorithm for mapping Direct Normal Irradiance (DNI) in thermosolar power plants using a Mobile Robotic Sensor Network (RSN) is presented. The algorithm selects measurement spots and allocates the RSN accordingly to carry out the dynamic estimation of DNI. A generic thermosolar power plant with a fleet of vehicles is used as a simulated case study to assess the performance of the algorithm. The results show that the proposed method allows us to obtain a spatial estimation of the DNI that could improve the performance of the plant, and that outperforms estimations based on a single pyrheliometer.

The escalation of energy demand in both developed and developing countries has prompted people to search and utilize clean and sustainable energy resources. Omshanti Retreat Centre (ORC) situated in Gurugram, Haryana, India utilizes solar energy to meet most of its energy demand. It has installed Scheffler dish type solar thermal cooking system having a capacity to cook 6000 meals per day in the year 2001 and is being utilized to cook food three times a day for approximately 200 days per year. The present research was conducted to study the performance of the solar thermal cooking system both experimentally and analytically; identify areas from where heat losses are occurring in the system and suggest steps that should be taken for improvement of overall system performance. Experiments were performed for seven consecutive days at ORC during summer. The system consists of twenty eight collectors arranged in such a way that a pair of Scheffler reflectors of 10 m2 aperture area concentrates solar radiation on one absorber. Thermal efficiency of 70–80% was obtained for the collectors experimentally for an average direct normal irradiance of 800 W/m2. The overall system efficiency was estimated to be 25% which suggests significant losses in the overall process. The research identified the areas which act as sources for heat losses from the system.

The current research objective is to explore changes in solar radiation variables across the Indian region during the lock-down period. Global Horizontal Irradiance (GHI), Direct Normal Irradiance ...

Deployment of future CSP plants is foreseen to take place in southern Portugal, a region with great potential for solar energy harvesting. For the operation and maintenance of such power plants, it is important to know in advance the potential influence that soiling can have in second-surface mirrors, particularly in the specular reflection. For this reason, a study regarding the effects of soiling in second-surface mirrors’ specular reflection is carried out during a period of one year and a half. Moreover, and since soiling particle deposition can have a crucial economic impact for the energy management of CSP plants, it is important to analyze and establish a method to improve the cleaning operations of a CSP plant considering local conditions. The applied methodology is based in programming artificial cleaning profiles based on ground soiling effect and direct normal irradiance measurements, which are then introduced in a plant simulator to estimate the yearly annual energy production. The generated energy is then used to compare different profiles in face of any possible energy selling price and cleaning costs. The presented method is unique in comparison with current literature, due to its versatility, and can be used for any region.

Attenuation of broadband solar irradiance by aerosols has been traditionally evaluated in wide areas using aerosol optical depth (AOD) discretized at monthly time steps. This fact is known to induce a bias in the long-term solar irradiance evaluations. Nowadays weather models allow for a spatially-continuous description of AOD at sub-daily steps (e.g., hourly or 3-hourly). But considering the still high uncertainty of their modeled AODs in relation with the natural sub-daily AOD variability, a key question is whether the attenuation of solar irradiance by aerosols could simply be accounted for from daily AOD means. Hence, the bias induced by the non-resolved sub-daily AOD variability should be investigated. This work proposes a mathematical methodology to understand the roots of such a bias and uses AOD observations from 213 ground sites to investigate it. Overall, the mean and standard deviation of the bias at evaluating the daily mean global horizontal solar irradiance stay at about −0.2 and 1.2 W/m2, respectively, or −2.0 and 5.5 W/m2 for direct normal irradiance. The mean of the daily bias nearly vanishes using the proposed methodology while the standard deviation reduces to only 1.0 (3.3) W/m2 in global horizontal (direct normal) solar irradiance.

Accurate assessment of the solar irradiance reaching the absorber of concentrating solar power (CSP) systems is crucial for accurate energy generation estimates as well as for power plants design and operation. Due to a larger aperture angle, direct normal irradiance (DNI) measurements taken by pyrheliometers include circumsolar normal irradiance (CSNI), i.e., the diffuse radiance from the vicinity of the sun disk, which in some cases is not totally captured by the CSP systems because of their lower aperture angles. This work reports the modelling of DNI and CSNI using a radiative transfer model and atmospheric measurements and aims to develop a fast and simple model to estimate the CSNI based on more common solar irradiance measurements such as the DNI, global horizontal irradiance (GHI) and diffuse horizontal irradiance (DHI). The proposed model performs better than other models available in the literature for the six locations analysed (scattered around the globe), thus contributing to a more accurate solar resource assessment and CSP systems design and operation.

A new aerosol direct transmittance parameterization is developed based on detailed radiative transfer model calculations for improving direct normal irradiance (DNI) for the solar energy forecast in the fast radiation scheme SUNFLUX. The modified SUNFLUX scheme is evaluated against observational datasets collected from the 14 Baseline Surface Radiation Network (BSRN) stations. The aerosol data from AERONET stations collocated with the BSRN network are used in the evaluations. The evaluation results have indicated that the accuracy of the calculated DNI using the new version of SUNFLUX has been improved significantly compared to the old version. The relative mean bias difference (MBD) and relative root mean square difference (RMSD) are −3.03% and 4.95%, respectively. The performance of the modified DNI model is also compared with those from the other five models. The results predicted by the current model are comparable with those from the best reference models. More importantly, the evaluation results show that the new aerosol scheme developed in this study can also be applied to other models to improve their performance.

Since the fossil reserves are depleting day by day, the trend of modern energy sector is going towards renewable energy. The demand of solar power plants is therefore at the peak nowadays across the globe. However, the construction of these plants is extremely dependent on feasibility study to estimate the real solar potential before installing it in any region. To evaluate the solar energy potential of Peshawar region in Pakistan, Ground-based global horizontal irradiance (GHI) and direct normal irradiance (DNI) were compared with satellite-based model SUNY. Ground measurements were done at the University of Engineering and Technology Peshawar (UET Peshawar) with the help of pyranometer and shadowband irradiometer. Comparison of the data showed that there was a maximum difference of 42.90% in ground and satellite-based GHI in the month of December. Minimum difference in GHI was found for the month of March that was −3.83%. Moreover, ground-based GHI was overestimated in the month of February, March, and April, while in rest of the months, satellite values of GHI exceeded the ground measurements. Similarly, maximum difference of 55.86% was found in the month of November between ground and satellite-based DNI while minimum difference of −3.34% was seen in DNI in the month of March between the two data. Furthermore, satellite-based DNI was underestimated in the months of February, March, and April while in rest of the months it was overestimated compared to ground measurements. In addition to this, correlation of ground and satellite-based GHI and DNI showed R2 value of 0.8852 and 0.4139, respectively. The results of this study revealed that the difference between ground measurements and satellite values was considerable and hence real time measurements are necessary to properly estimate solar energy resource in the country.

Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.

Recent progress in radiometry allows the development of diffuse fraction models based on data stored at high-frequency sampling. At the present only a handful of such models exist in the literature. In this paper, we present a new and simple model for estimating diffuse fraction developed on 1-minute resolution data. The new model (PB) is extensively validated on data from the Baseline Surface Radiation Network, for both diffuse fraction (kd) and direct normal irradiance (Gn). The results show that PB performs well compared to other similar models, despite its structural simplicity. kd is estimated with the best accuracy in locations with temperate climate (TM), the average (median) nRMSE of the tested TM stations being 18.38% (17.79%). The Gn estimates are found to be most accurate at arid locations (AR), the average (median) nRMSE over all tested AR stations being 17.29% (16.96%). We find that the performance of the models varies strongly as a function of the climate zone or when we switch between estimating diffuse and direct normal irradiance components, possibly changing the accuracy-based rankings among models, a fact which should be taken into consideration by users when selecting appropriate models.

Cloud cover and cloud motion have a large impact on solar irradiance. One of the effective ways for direct normal irradiance (DNI) prediction is to use cloud features, which has been extensively studied. A Convolutional Neural Network (CNN) has the advantage of automatic features extraction by using strong computing capabilities. In this paper, a novel 3D-CNN method is proposed by processing multiple consecutive ground-based cloud (GBC) images in order to extract cloud features including texture and temporal information. The resulting features and the DNI data are then used to establish a DNI forecasting model. The experiments are carried out to evaluate the performance of the proposed forecasting method by using the data from January 1, 2013 to December 31, 2014. The experimental results show that the proposed method coupled with the multilayer perceptron (MLP) model achieves forecast skill of 17.06% for 10-minute ahead DNI prediction.

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system’s accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system’s accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich.

Under cloud-free skies, aerosols are mainly responsible for controlling the observed Direct Normal Irradiance (DNI) variability for studying and designing Solar Concentrating Technologies. In this study, the induced uncertainties on cloud-free DNI due to the use of aerosol optical properties from the CAMS reanalysis project (AODCAMS) are investigated. DNI is calculated using a radiative transfer model while a pre-comparison and calibration of aerosol reanalysis products are performed using as skill reference the observations from AERONET. The site-specific validation showed an overestimation of the AOD in locations affected by volcanic eruptions while underestimation and higher dispersion metrics are revealed in regions dominated by coarse aerosols from mineral dust and biomass burning. A pre-calibration process of AODCAMS significantly improved the error metrics in all AERONET locations providing significant reductions in all ‘hot-spot error’ regions. In terms of DNI, using the initial AODCAMS, rMBE extends between −28% and 30%, exhibiting higher magnitudes in South and South-Eastern Asia. Using the calibrated AODCAMS, the pre-described error metrics are significantly improved in site-specific and regional scales.

Made available in DSpace on 2021-06-25T11:11:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-06-01 The interest of the direct normal irradiation (DNI) estimation is important in the evaluation of the solar potential and, consequently, for data correction and expansion of the historical series. In this study, a review of the performance of 16 models of radiative transfer was performed. These models are used to estimate DNI on a clear day in Botucatu/SP region located in Brazil. The revised models are categorized into two classes: simple models (11 models: ASH, MAJ, ALLEN, GH, P1, HLJ, FU, KU, H1, IP, and INC) and complex models (five models: BIRD, IQ, MRM5, P2, and YANG). The evaluation methodology used here is consistent with the literature. The input parameters were estimated and a statistical analysis using relative-mean-bias-error (rMBE), root-mean-square-error (rRMSE), and mean absolute percentage error (MAPE) indicators were performed to validate those models. The results indicate that the complex models (that require more atmospheric inputs) generally performed better than simpler models. Despite the consistent limitations in the use of estimated parameters, the performance of the models can be considered satisfactory. The best performances are highlighted for models MRM5 and YANG. Simple models ASH and IP performed similar to complex models. These results were confirmed using frequency distribution and the cumulative frequency analysis. These results are important for engineers of solar systems to use the best model and select the most suitable locations for installing a small or large solar plant. Faculty of Agronomic Engineering UFPA, Rua 9 Coronel Jose Porfırio, n° 2515 Saõ Paulo State University UNESP Federal University of Mato Grosso do Sul Department of Mechanical Engineering University of M'Sila Saõ Paulo State University-UNESP Saõ Paulo State University UNESP Saõ Paulo State University-UNESP

To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of ≈0.84 (i.e. an increase of ≈27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of ≈0.78 (i.e. an increase of ≈6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant’s profit in ≈0.44 M€/year, as compared with the original forecasts. Operational strategies are proposed accordingly.

Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence between these factors and irradiance. In this paper, we introduce the idea of applying multivariate Gated Recurrent Units (GRU) to forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated against traditional Long Short-Term Memory (LSTM) using historical irradiance data (i.e., weather variables that include cloud cover, wind direction, and wind speed) to forecast irradiance forecasting over intra-hour and inter-hour intervals. Our evaluation on one of the sites from Measurement and Instrumentation Data Center indicate that both GRU and LSTM improved DNI forecasting performance when evaluated under different conditions. Moreover, including wind direction and wind speed can have substantial improvement in the accuracy of DNI forecasts. Besides, the forecasting model can accurately forecast irradiance values over multiple forecasting horizons.

International audience; This study deals with a numerical investigation to assess and compare the thermal and economic performance of two solar tower power systems. It concerns the Molten Salt (MS) and Direct Steam Generation (DSG) technologies used as heat carrier and storage. For this purpose, a 50 MWe solar tower plant without thermal energy storage under the climatic conditions of the eastern Moroccan region is simulated with the System Advisor Model (SAM) software. The meteorological data has been collected via a high precision meteorological station located in Oujda city(34°40'53'' N 1°54'30.9'' W). The results are presented in terms of monthly energy production, annual energy output, and Levelized Electricity Cost (LEC). From these findings, it can be concluded that, for an amount annual Direct Normal Irradiance (DNI) of 1989.9 kWh/m2/yr, the molten salt plant has the highest annual energy production than the DSG (86.3 GWh for MS against 83.3 GWh for DSG) and the LEC of the Molten salt plant is 12.5 % lower than the DSG plant. ©2020. CBIORE-IJRED. All rights reserved

This paper epitomizes the analysis of the solar resource measurements and a simulation of curved thin-film modules for photovoltaic applications. The analysis used 1 year (January 1, 2018 until December 31, 2018) of data from 7 stations distributed across the eastern province (KSA) based on measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters. The 7 stations show the results obtained for the annual average daily GHI over the study period shows a variation of almost 300 Wh/m2 among station, with mean daily total values in the eastern region 6755 Wh/m2. The results above indicate that photovoltaic would perform well at any location although extreme high temperatures in some locations may degrade the performance of some types of photovoltaic. Annual average daily DNI all above 5500 Wh/m2. The average daily total DHI over the study period among stations is 2840 Wh/m2. Second part of study uses two-step model, 1D and 3D model. The 1D describes the technology and outputs device (current density, temperature, illumination), 3D model uses this data as one of its inputs, and describes size, shape within the curved flexible module.

The increasing energy demand encourages the use of photovoltaic solar systems coupled to organic rankine cycle (ORC) systems. This paper presents a model of an ORC system coupled with a concentrating photovoltaic and thermal (CPV/T) system. The CPV/T-ORC combined system, described and modeled in this paper, is sized to match the electrical load of a medium industrial user located in the South of Italy. A line-focus configuration of the CPV/T system, constituted by 16 modules with 500 triple-junction cells, is adopted. Different simulations have been realized evaluating also the direct normal irradiance (DNI) by means of the artificial neural network (ANN) and considering three input condition scenarios: Summer, winter, and middle season. Hence, the energy performances of the CPV/T-ORC system have been determined to evaluate if this integrated system can satisfy the industrial user energy loads. In particular, the peak power considered for the industrial machines is about 42 kW while other electrical, heating or cooling loads require a total peak power of 15 kW; a total electric average production of 7500 kWh/month is required. The annual analysis shows that the CPV/T-ORC system allows satisfying 100% of the electric loads from April to September; moreover, in these months the overproduction can be sold to the network or stored for a future use. The covering rates of the electrical loads are equal to 73%, 77%, and 83%, respectively for January, February, and March and 86%, 93%, and 100%, respectively for October, November, and December. Finally, the CPV/T-ORC combined system represents an ideal solution for an industrial user from the energy point of view.

Concentrating photovoltaic and thermoelectric solar facilities base their operation on collecting the direct component of solar radiation. Given that the direct beam that reaches the Earth's surface varies greatly in time and space, it is common to assist the bankability of projects with a solar resource assessment. Sun-tracking collector plants are typically examined via a time series analysis of measured weather data and test reference years. Such analysis, which considers the eventual presence of clouds, may be complemented with the use of the synthetic clear-sky year assuring the maximum theoretical availability of direct normal irradiance at a site. This work introduces for the first time the concept of site-adapted clearest-sky year (CSY) and provides a methodology for its generation. Three methods to build the CSY and one algorithm to detect clear-sky moments are proposed.

There are different processes for hydrogen production and different sources of energy could be used to drive these processes. However, a hydrogen production technique won't insure market penetration unless it succeeds in securing the economic competitiveness. In the present work, techno-economic study of the solar-electrolysis based hydrogen production techniques is carried out. The present study is limited to the case of hydrogen production using a hybrid solar parabolic trough-gas power plant-electrolysis system. The effect on hydrogen production cost of different factors, such the direct normal irradiance and the solar fraction are investigated. This technique of hydrogen production is considered for the case of Algeria. In order to determine the effect of the climatic conditions on the cost of hydrogen production, two different sites have been chosen: one in Northern Algeria and the other one in Southern Algeria. The results indicate that the cost of hydrogen production is dominated by the cost of hydrogen related to the cost of energy production. This cost is highly dependent on solar fraction and solar insolation. The results show also that solar CSP based hydrogen production technique is more competitive than the conventional PV based hydrogen production technique but as competitive or less competitive than CPV based hydrogen production technique.

Kenya is faced with a rising demand in electricity resulting from a rapidly growing economy and an increasing population. Being a tropical country, lying astride the equator, solar energy is one of the readily available renewable energy resource options to meet this need. Unfortunately, there is still very low adoption of solar systems in the country which could be majorly attributed to lack of adequate solar resource assessment. Besides, past studies" on this area in Kenya only focused on the available amount of solar resource leaving out the issue of variability. To bridge this gap, the temporal and spatial variability of global horizontal irradiance (GHI) and direct normal Irradiance (DNI) is analyzed using 19-year long (1995-2013) Meteosat satellite dataset. GHI interannual variability is low in most parts of the country but DNI has a clearly higher variability except a few locations in the East and Northern desert. Low spatial variability for GHI was recorded for locations within 1225 km(2) while DNI variability was double that of GHI. The results offer readers a quick reference of variability of solar resource at different locations in Kenya which is useful in guiding measurement requirements and consequently in promoting deployment of solar systems. (C) 2017 The Authors. Published by Elsevier Ltd.

When modeling the effect of the cloud transients in the Direct Normal Insolation (DNI), it is particularly relevant to identify those moments in which there are no clouds between the observer and the sun. In this paper, we present a simple algorithm for offline detection of situations where the sun path to the observer is not obstructed by any cloud. The algorithm is based on the characterization of the relations between the measured and the clear sky curves. The clear sky identification module consists of three evaluation and detection metrics: hourly mean, slope, and line length criterion. All of them rely on the assessment of the measured data against the clear sky generated data. The conjunction of the fulfillment of the three criteria leads to the clear sky hour identification. We validate our algorithm by comparing our results with those obtained from a recently published clear sky detection algorithm that uses high temporal resolution Global Horizontal Irradiation (GHI) as the input. We obtain a 98% agreement when having more than 50 min identified as clear.

One possible way to further reduce levelized costs of electricity of concentrated solar thermal energy is to directly use water/steam as the primary heat transfer fluid within a concentrated collector field. This so-called direct steam generation offers the opportunity of higher operating temperatures and better exergy efficiency. A technical challenge of the direct steam generation technology compared to oil-driven power cycles is a competitive storage technology for heat transfer fluids with a phase change. Latent heat thermal energy storages are suitable for storing heat at a constant temperature and can be used for direct steam generation power plants. The calculation of the economic impact of an economically optimized thermal energy storage system, based on a latent heat thermal energy storage system with phase change material, is the main focus of the presented work. To reach that goal, a thermal energy storage system for a direct steam generation power plant with parabolic troughs in the solar field was thermally designed to determine the boundary conditions. This paper discusses the economic impact of the designed thermal energy storage system based on the levelized costs of electricity results, provided via a wide parametric study. A state-of-the-art power cycle with a primary and a secondary heat transfer fluid and a two-tank thermal energy storage is used as a benchmark technology for electricity generation with solar thermal energy. The benchmark and direct steam generation systems are compared to each other, based respectively on their annual electricity yields and their levelized costs of electricity at two different sites. A brief comparison of a passive and an active latent heat storage system is included. Finally, specific target costs for the new direct steam generation thermal energy storage system are determined based on the results of the parametric study.

We have developed a method for generating synthetic five-minute pairs of global horizontal irradiance (GHI) and direct normal irradiance (DNI), interpolated from hourly mean values. Our method develops a model based on five-minute pairs of GHI and DNI from a location where real five-minute data is available. Five-minute GHI and DNI clear sky index values are calculated for each five-minute time period, and hourly values are calculated for each hourly period. The hourly values identify a clear sky category into which the twelve five-minute GHI and DNI clear sky index values are placed. The result is clear sky categories each containing five-minute clear sky index values that capture how five-minute GHI and DNI fluctuates for each clear sky category. The process for generating synthetically interpolated five-minute pairs of GHI and DNI from hourly mean values is straightforward. Hourly GHI and DNI clear sky index values are used to identify the clear sky category from which twelve five-minute synthetic pairs of GHI and DNI clear sky index values are bootstrapped. The index values are multiplied by the modelled perfect clear sky GHI and DNI to give synthetically interpolated five-minute pairs of GHI and DNI. Our method performs well. The hourly total Normalised Root Mean Square Deviation (NRMSD) results for our method are 1.4–1.8% for GHI and 2.2–3.0% for DNI. The hourly total Mean Bias Deviation (MBD) results for our method are −0.6% to 0.2% for GHI and −1.5% to −0.2% for DNI. The R 2 values between observed and synthetic hourly totals are all greater than 0.99. The monthly Kolmogorov-Smirnov Integral (KSI) values were good for 97% of all months for DNI and for all months for GHI. Our method can be applied to locations where only hourly data is available. For example, it could be applied to the Australian Bureau of Meteorology’s hourly solar irradiance gridded satellite data, to capture the spatial variation in solar irradiance for multiple locations. The result would be particularly useful, from a solar energy perspective, for designing a reliable renewable energy electricity system where its performance could be evaluated for any location in Australia.

Direct normal irradiance (DNI) is vital for concentrated solar thermal plants, and its value under clear sky condition is usually used as an important input of some forecasting models for solar irradiance. First of all, a semi-empirical model is developed for calculating the clear-sky DNI, and it is used to transform DNI into clear-sky index to remove the impact of the sun's position on DNI. Then, a wavelet forecasting model, using the clear-sky index as input, is proposed for estimating the inter-hour DNI under any sky conditions. The non-stationary series of the clear-sky index is decomposed by wavelet technology to obtain four sub-series with different frequencies, and forecasting sub-models are constructed according to the features of corresponding sub-series, respectively. Finally, model validation is conducted with data from the open database of the National Renewable Energy Laboratory. The result shows that the performance of the clear-sky model is satisfactory comparing with some other published clear-sky models, and that the wavelet forecasting model achieves great performance with nMAE = 0.84–7.66% and nRMSE = 1.89–10.99% for four different stations and also outperforms other published forecasting models.

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents an initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.

Day-ahead forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS), the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), are used to simulate a concentrating solar power (CSP) plant through the System Advisor Model (SAM) to assess the potential value of the IFS in the electricity market. Although DNI forecasting from the IFS still demands advances towards cloud and aerosol representation, present results show substantial improvements with the new operational radiative scheme ecRad (cycle 43R3). A relative difference of approximately 0.12% for the total annual energy availability is found between forecasts and local measurements, while approximately 10.6% is obtained for the previous version. Results of electric energy injection to the grid from a simulated linear focus parabolic-trough system shows correlations coefficients of approximately 0.87 between hourly values of electric energy based on forecasted and measured DNI, while 0.92 are obtained for the daily values. In the context of control strategy, four operational strategies are given for different weather scenarios to handle the energy management of a CSP plant, including the effect of thermal energy storage capacity. Charge and discharge operational strategies are applied accordingly to the predicted energy availability.

Knowledge on the diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) is crucial for the estimation of the irradiance on tilted surfaces, which in turn is critical for photovoltaic (PV) applications and for designing and simulating concentrated solar power (CSP) plants. Since global horizontal irradiance (GHI) is the most commonly measured solar radiation variable, it is advantageous for establishing a suitable method that uses it to compute DHI and DNI. In this way, a new model for predicting the diffuse fraction ( K d ) based on the climate zone is proposed, using only the clearness index ( K t ) as the predictor and 1-min resolution GHI data. A review of the literature on models that use hourly and sub-hourly K t values to compute K d was also carried out, and an extensive performance assessment of both the proposed model and the models from the literature was conducted using ten statistical indicators and a global performance index (GPI). A set of model parameters was determined for each climate zone considered in this study (arid, high albedo, temperate and tropical) using 48 worldwide radiometric stations. It was found that the best overall performing model was the model proposed in this work.

Accurate estimations of clear-sky direct normal irradiance (DNIcs) and diffuse horizontal irradiance (DIFcs) are crucial in solar resources assessment. This study examines 95 and 88 popular clear-sky irradiance models for their worldwide estimation capability of DNIcs and DIFcs, respectively. Atmospheric inputs are from MERRA-2 reanalysis and irradiance observations for validation are extracted from 100 ground stations across five major Koppen-Geiger climate zones. They consist of 24 million 1-min measurements of DINcs and 18.7 million 1-min measurements of DIFcs after quality control and clear-sky detection, during the 5-year period 2015–2019. Using principal component analysis, the performance of each clear-sky irradiance model is ranked separately among the five climate zones, as well as given a global rank. For the Equatorial, Arid, Temperate, Cold and Polar climates, it is found that Heliosat1-I , ESRA-I , REST2v9.1 , REST2v9.1 and CLS , respectively, are the best DNIcs models, while Modified Iqbal-C , Heliosat1-R , Calinoiu , Modified Iqbal-C and PSI-REST are the best DIFcs models. On a global worldwide basis, the three top-ranking models are REST2v9.1 , REST2v5 and MMAC-V2 for DNIcs, and Modified Iqbal-C , PSI-REST and MRMv5 for DIFcs. The results and rankings presented are strictly relative to MERRA-2 input data, and should not be extrapolated to results from alternate sources of atmospheric data. This detailed validation exercise revealed inconsistent performance of many models across different climates, possibly due to insufficient training and/or over-fitting of empirical relationships. Codes of all clear-sky irradiance models in the public domain are available online on the Github repository JamieMBright/clear-sky-models .

Integrated solar combined-cycle (ISCC) system has better thermal performance than the original gas steam combined-cycle system and a lower initial investment than stand-alone solar thermal plants. However, due to the uncertainty of meteorological conditions, the operation condition of the ISCC system changes continuously. In this study, a dynamic model of the ISCC system is built, and the operation strategies of two ISCC systems with and without the heat storage system are proposed. Accordingly, the dynamic performances of these two ISCC systems on a typical day are compared and analyzed, and the response characteristics of main operation parameters, such as main steam parameters and solar energy power generation, concerning direct normal irradiance (DNI) are obtained. The results show that the stability and security of the ISCC system with a heat storage system are superior to those without the heat storage system during the operation. To verify whether the operation strategy of the ISCC system with heat storage system is universally adaptable under different meteorological conditions, the dynamic performances of the ISCC system are further studied on a good-weather day with a strong DNI and a bad-weather day with weak DNI, respectively.

This paper presents one of the first discussions on the new Solar Atlas of Saudi Arabia, which was launched in February 2014. It assesses selected solar resource and surface meteorological measurements available from the new Renewable Resource Atlas for Saudi Arabia developed by the King Abdullah City for Atomic and Renewable Energy (KACARE) as part of the Renewable Resource Monitoring and Mapping (RRMM) Program. The Solar Atlas provides live data recorded from 41 stations across the country. Accurate solar resource data is critical in reducing technical and financial risks of deploying utility-scale solar energy conversion systems. This paper presents solar maps of the country over time, showing that the direct normal irradiance (DNI) in various regions of the country ranges from approximately 9000 W h/m 2 /day in the summer months to 5000 W h/m 2 /day in the winter months. Global horizontal irradiance (GHI) in various regions can be as high as 8.3 kW h/m 2 /day. One year of ground based solar radiation measurements from new stations at KACARE Headquarters Riyadh and Qassim University are compared with satellite-based model estimates of long-term DNI and GHI solar resources on a monthly mean daily total basis. Comparing the locally measured DNI data at KACARE Headquarters Riyadh with GeoModel, the average difference is about 6.9% of the reported values, the average measurement uncertainty being 8.4%. For GHI measured at KACARE Headquarters Riyadh, a fairly good agreement can be seen between the RRMM values and GeoModel with the average difference being about 5.6%, while the average measurement uncertainty being 6.7%. It might be noted that the GeoModel has data uncertainties for monthly mean daily total radiation in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI. Finally, the paper reviews data from the recording station at Qassim University (QU), which is equipped with a variety of instruments for measuring solar radiation and surface metrological conditions. The measured values of DNI at QU varied from a maximum of 8367 W h/m 2 /day in July to a minimum of 4702 W h/m 2 /day in January. In terms of percentages, the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. In most cases, the observed differences between measured and modeled data were within the combined estimated uncertainty. The results from Solar Atlas are critical in guiding policies, reducing the risks for deploying solar facilities and providing judicious information for construction of solar facilities.

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). The coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Koppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.

High-quality direct normal irradiance (DNI) observations are of vital importance for the optimal design, installation and profitability of CST plants. Numerous models have been developed for estimating DNI at regional or global scales. The goal of this study is to generate a gridded DNI dataset for all-sky conditions over mainland China during 1981–2014, based on a broadband DNI estimates for clear-sky conditions (obtained with the REST2_v9.1 model) and cloud transmittance estimates using sunshine observations. The results indicate that the REST2_v9.1 model can be used to estimate DNI with high accuracy and consistency, owing to its robust two-band parameterization of the radiation transfer processes. Comparing daily DNI modeled predictions to measurements at 6 BSRN (Baseline Surface Radiation Network) stations in East Asia results in relatively low errors statistics: RMSE, MAE, RMSER, MAER and R of 1.436 MJm−2, 0.900 MJm−2, 22.26%, 13.95% and 0.972. Somewhat lessened agreement is found at the 17 CMA (China meteorological administrations) stations: 4.064 MJm−2, 2.864 MJm−2, 34.41%, 24.26% and 0.914, respectively. A gridded DNI dataset is constructed using sunshine duration measurements at 2474 CMA meteorological stations and the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis products. The spatial and temporal variations of DNI in different climate zones throughout China are also investigated. The gridded DNI datasets generated in this study would assist in numerous solar resource studies and solar energy applications.

This study aims to improve operational day-ahead direct normal irradiance (DNI) forecasts in clear-sky conditions using the Weather and Research Forecasting model. To create three different forecasting methods targeting the direct effect of aerosols on radiation, we use three different types of aerosol optical depth (AOD) data: (1) the Tegen aerosol climatology, (2) the persistence of measured AERONET AOD, and (3) the Goddard Earth Observing System model version 5 (GEOS-5) gridded forecasts of AOD. We evaluate each method at the Solana Generating Station, a concentrating solar power plant near Gila Bend, Arizona, and the University of Arizona, Tucson. We perform a retrospective DNI forecast analysis and find that including GEOS-5 forecast AOD improved the DNI forecast compared to using an aerosol climatology at both locations. At Tucson, where AOD is measured, we find that the persistence of measured AOD gives the best DNI forecast. However, the accuracy of that measured AOD reduces when translating it 225 km to Solana to forecast DNI 48 hours later. We then include the GEOS-5 AOD forecasts in one member of an operational forecast system and evaluate it against the other ensemble members that use the aerosol climatology. In clear-sky conditions, including GEOS-5 forecast AOD instead of the Tegen aerosol climatology, reduces the DNI forecast root mean square error by 27% at Solana. We found no significant differences during all-sky conditions because the relatively poor performance during cloudy conditions outweighs the improvements made in clear-sky conditions.

International audience; Routine measurements of the broadband beam irradiance at normal incidence by means of pyrheliometers or equivalent pyranometric systems include unknown contributions from the irradiance originating from within the extent of the solar disc Bnsun, and that from a larger circumsolar region defined by its solid angle aperture, called the circumsolar normal irradiance CSn. This article describes a fast and simple parametric model that estimates the beam and circumsolar radiation, for opening half-angles in the interval [0.4°, 5°], under cloud-free conditions in a desert environment. 1 min measurements of the beam normal Bn, global G and diffuse D horizontal irradiances at Solar Village, Saudi Arabia, and Tamanrasset, Algeria, were used for calibration and validation. Using AERONET measurements as inputs to the radiative transfer code libRadtran, it has been checked–through an ‘indirect’ validation with the ground measured Bn–that the modelled Bnsun and CSn are accurate. Accordingly, a library of Bnsun and CSn modelled by libRadtran for varying solid angle apertures was generated. Building on this library, a fast parametric model was developed to estimate Bnsun and CSn using G, D and Bn as inputs. The coefficients of the model were fitted to a training set of measurements and then validated twice: once at their respective site, and once at the other site. When using the coefficients for their own site for both Solar Village and Tamanrasset for CSn, the relative bias is respectively −2.7% and −1.5%, the relative root mean square error (RMSE) is 19.9% and 19.6%, and the correlation coefficient is 0.871 and 0.935. As for Bnsun, the relative bias is −2.0% and −2.2%, the RMSE is 2.7% and 3.6%, and the correlation coefficient is 0.999 and 0.998. Applying the coefficients of one site to the other site yields satisfactory results. It is recommended to use the coefficients of Tamanrasset for desert sites exhibiting frequent clear skies, and those of Solar Village for sites exhibiting frequent turbid skies. The coefficients have also been fitted using data from both sites, for a combined model.

A separation model was developed for deriving the direct normal irradiance (DNI) and the diffuse horizontal irradiance (DHI) from the global tilted irradiance (GTI). The model is based on the DIRINT model, but substitutes the use of the GTI in place of the global horizontal irradiance (GHI) for determining the global clearness index ( K t ). Additionally, an iterative method is used to adjust K t to improve the derived values of the DNI and the DHI. The model is referred to as the GTI-DIRINT model. When using for model input the GTI measured with a pyranometer tilted at a small angle from the horizontal (10°), the deviations between the measured DNI and DHI and the GTI-DIRINT-modeled DNI and DHI were essentially the same as those for the DIRINT model when using the GHI for model input. Increasing the tilt angle from horizontal (as great as 44° for Eugene, Oregon) increased the deviations between the modeled and measured DNI and DHI, but the results were still reasonable. The GTI-DIRINT model provides reasonable values of DNI and DHI from the GTI, and if they are subsequently used with the Perez tilted surface model to model the GTI for orientations within 30° of the azimuth and tilt of the GTI used for model input, the result is as good or better than may be obtained when using a measured DNI and DHI, and without their significant instrumentation cost.

A methodology is presented to determine both the short-term and the long-term influence of the spectral variations on the performance of Multi-Junction (MJ) solar cells and Concentrating "This is the peer reviewed version of the following article: R. Nunez, C. Dominguez, S. Askins, M. Victoria, R. Herrero, I. Anton, and G. Sala, “Determination of spectral variations by means of component cells useful for CPV rating and design,” Prog. Photovolt: Res. Appl., 2015., which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/pip.2715/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving [http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms]." Photovoltaic (CPV) modules. Component cells with the same optical behavior as MJ solar cells are used to characterize the spectrum. A set of parameters, namely Spectral Matching Ratios (SMRs), is used to characterize spectrally a particular Direct Normal Irradiance (DNI) by comparison to the reference spectrum (AM1.5D-ASTM-G173-03). Furthermore, the spectrally corrected DNI for a given MJ solar cell technology is defined providing a way to estimate the losses associated to the spectral variations. The last section analyzes how the spectrum evolves throughout a year in a given place and the set of SMRs representative for that location are calculated. This information can be used to maximize the energy harvested by the MJ solar cell throughout the year. As an example, three years of data recorded in Madrid shows that losses lower than 5% are expected due to current mismatch for state-of-the-art MJ solar cells.

The DNI (Direct Normal Irradiance) is the resource utilized by solar concentrators, in CSP and CPV applications. However, it is not often measured due to practical reasons, and is derived instead from measurements of global irradiance or satellite-based models. This paper describes a study of different estimation procedures for the assessment of the DNI, using experimental data with a time scale of 1 min, taken at two different latitudes in Italy. The analyzed approaches include measuring techniques and models. Accurate estimations of DNI are obtained as derived from measurements of global horizontal irradiance and diffuse horizontal irradiance if the offset in pyranometers are properly corrected and the zenith angle is not too high. The measured and calculated values of DNI are used to estimate the hourly, daily, monthly and seasonal irradiation. The results show that the different estimation methods can lead to quite different conclusions when comparing short-term and long-term solar radiation availability. © 2015 Elsevier Ltd

A simple method for correcting satellite-derived direct normal irradiance, with important deviations to the experimental data, is presented in this work and illustrated for the particular case of Rajasthan (India). Northwest India is expected to have a high level of solar radiation and it is an interesting area for solar concentrating power systems. However uncertainty in direct normal irradiance estimations from satellite may affect negatively to the bankability of solar plants. Direct normal irradiance have been estimated for a site in Rajasthan from satellite information for the period 2003 to 2011, and ground measurements during 2011 have been used to analyze the uncertainties and to develop a simple correction method. The original satellite estimations showed important deviations from the experimental values and high bias. A systematic underestimation of direct irradiance has been observed during the dryer seasons that could be attributed to an overestimation of the aerosol optical depth input to the model. These observations have allowed the design of a correction methodology. Unbiased new estimations of direct normal irradiance have been generated with this methodology with important reduction in the deviations and with an agreement in the distribution functions compared to the distribution function of the ground data.

As part of an on-going program to develop technological scenarios for 100 per cent renewable generation within the Australian National Electricity Market (NEM), we explore the degree to which concentrating solar thermal (CST) power might reliably contribute to the generation mix. We analyse satellite-derived hourly direct normal irradiance data provided by the Australian Bureau of Meteorology for Australia over a 13-year period. This large data set covers sufficient time to enable us to characterise the frequency and duration of rare events such as extended periods of heavy cloud cover and hence low solar insolation over regions of Australia. The results highlight those regions with both the highest and lowest occurrence of extended periods of low DNI. They also identify regions whose correlated climatic characteristics would reduce overall CST generation variability if the plants were distributed across them. As such, the findings may assist both project developers, and long-term system planning for reserve generation capacity in future high renewable generation mixes.

Reliable solar radiation data are important for energy simulations in buildings and solar energy systems. Although direct normal irradiance (DNI) is required for simulations, in addition to global horizontal irradiance (GHI), a lack of DNI measurement data is quite often due to high cost and maintenance. Solar radiation models are widely used in order to overcome the limitation, but only a few studies have been devoted to solar radiation data and modeling in Korea. This study investigates the most suitable solar radiation model that converts GHI into DNI for Korea, using measurement data of the city of Daejeon from 2007 to 2009. After ten existing models were evaluated, the Reindl-2 model was selected as the best. A new model was developed for further improvement, and it substantially decreased estimation errors compared to the ten investigated models. The new model was also evaluated for nine major cities other than Daejeon from the standpoint of typical meteorological year (TMY) data, and consistent evaluation results confirmed that the new model is reliably applicable across Korea.

This study seeks to assess the potential of utilizing Concentrated Solar Power for electricity generation in Zimbabwe. Data from 26 meteorological stations which are widely distributed around the country was used to map the distribution of solar radiation. Geographic information systems were employed to locate sites with potential for installing concentrating solar power plants. Areas with good potential were identified by using the following assessment factors: direct normal irradiance (DNI), proximity to transmission lines and water bodies, flatness of the area and the vulnerability of vegetation and wild life. After considering all the assessment factors, a total area of 250 000 km 2 was found to be suitable. If only 10% of the suitable land area is used and the technology with the least efficiency (8–10%) is adopted, about 71.4 GW can be generated. The projected power generation is about thirty times the current power demand of the country.

In the context of the current rapid development of large-scale solar power projects, the accuracy of the modeled radiation datasets regularly used by many different interest groups is of the utmost importance. This process requires careful validation, normally against high-quality measurements. Some guidelines for a successful validation are reviewed here, not just from the standpoint of solar scientists but also of non-experts with limited knowledge of radiometry or solar radiation modeling. Hence, validation results and performance metrics are reported as comprehensively as possible. The relationship between a desirable lower uncertainty in solar radiation data, lower financial risks, and ultimately better bankability of large-scale solar projects is discussed. A description and discussion of the performance indicators that can or should be used in the radiation model validation studies are developed here. Whereas most indicators are summary statistics that attempt to synthesize the overall performance of a model with only one number, the practical interest of more elaborate metrics, particularly those derived from the Kolmogorov–Smirnov test, is discussed. Moreover, the important potential of visual indicators is also demonstrated. An example of application provides a complete performance analysis of the predictions of clear-sky direct normal irradiance obtained with six models of the literature at Tamanrasset, Algeria, where high-turbidity conditions are frequent.

The effect of spatial and temporal variability of aerosol optical depth (AOD) on direct normal irradiance (DNI) under clear skies is studied, with the synergetic use of satellite and ground-based data as well as calculations from a radiative transfer model. The area of interest is Europe; data from May to September during 13 years (2000–2012) are analyzed. The aerosol effect on DNI is high in areas influenced by desert dust intrusions and intense anthropogenic activities, such as the Mediterranean basin and the Po Valley in Italy. In May, the attenuation of DNI from aerosols, over these areas, can reach values up to 35% and 45% respectively, which corresponds to 4 and 6 kWh m−2 per day. In most areas, even for periods with lower values of AOD, the attenuation of DNI is found to be around 20%, which corresponds to about 2–3 kWh m−2 less received DNI per day, compared to the corresponding value on an aerosol clean day. However, the DNI has increased during the recent years, due to the decreasing tendency of AOD over most areas of Europe. The increase is around 6–12%, which corresponds to an amount of 0.5–1.25 more kWh m−2 received per day, compared to a clean day. The percentage differences of daily DNI from the corresponding monthly climatological value reveals that day-to-day differences (due to AOD changes) from the monthly mean, by ±20%, can occur. The significance of the aerosol changes in Europe reveals the necessity for near real-time measurements or forecasts of AOD when reliable estimations of DNI are required.

The aim of this paper is to find a simple model to predict the maximum power of a high concentrator photovoltaic (HCPV) module under real conditions from easily obtainable atmospheric parameters. The model is based on a simple easy to use equation. This equation is the function of direct normal irradiance and air temperature and has an additional correction based on air mass. In order to define this equation, two HCPV modules from different manufacturers were measured over a period of two years to provide a complete set of measurements, representing the behaviour of HCPV modules in different outdoor conditions. The proposed model shows an adequate match between actual and estimated data for the particular HCPV modules located in Southern Spain.