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The development of solar energy at a regional scale necessitates a thorough understanding of available resources. Cuba, facing prolonged economic, environmental, and energy crises, urgently needs to enhance its sustainability through solar energy. Although solar resource mapping is widespread, Cuba lacks extensive field measurements, often relying on models that may not be ideally suited for large regions, like Matanzas province. Spanning over 12,000 km² with nearly 150 km between its northern and southern extremes, Matanzas presents challenges for high-resolution solar mapping. This study introduces a methodology that integrates various methods and databases to achieve the maximum resolution in the resulting solar map. This approach is designed for large areas, where conventional high-resolution models fall short. By optimizing calculation times and parameterizing the entire surface latitudinally, a high-resolution solar resource map for Matanzas has been developed. This map significantly enhances the understanding of solar resources in Cuba and enables the proposal of new methodologies for analyzing solar potential in similarly large regions. [ABSTRACT FROM AUTHOR]

Intermittent solar irradiance due to passing clouds poses challenges for integrating solar energy into existing infrastructure. By making use of intrahour nowcasts (very short‐term forecasts), changing conditions of solar irradiance can be anticipated. All‐sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. Herein, a deep learning (DL) model for solar irradiance nowcasts based on the transformer architecture is presented. The model is trained end‐to‐end using sequences of sky images and irradiance measurements as input to generate point‐forecasts up to 20 min ahead. Further, the effect of integrating this model into a hybrid system, consisting of a physics‐based model and smart persistence, is examined. A comparison between the DL and two hybrid models (with and without the DL model) is conducted on a benchmark dataset. Forecast accuracy for deterministic point‐forecasts is analyzed under different conditions using standard error metrics like root‐mean‐square error and forecast skill. Furthermore, spatial and temporal aggregation effects are investigated. In addition, probabilistic nowcasts for each model are computed via a quantile approach. Overall, the DL model outperforms both hybrid models under the majority of conditions and aggregation effects. [ABSTRACT FROM AUTHOR]

Soiling can greatly reduce both the efficiency of photovoltaic (PV) installations and the signals of radiometers. The knowledge of the current soiling losses of a PV installation can be used to optimize the cleaning schedule and to avoid false alarms related to other issues that might cause underperformance. Underperformance can be detected by comparing measured to modeled PV production derived using pyranometer or reference cell measurements. Soiled pyranometers or reference cells lead to too low modeled PV production so that PV soiling or other errors might not be detected. So far, soiling sensors either require frequent cleaning or they use indirect measurements to derive the soiling loss (e.g., analysis of backscattering signal or imaging of dust on a glass surface). Currently, the soiling loss of pyranometers or outdoor reference cells uses the comparison to another frequently cleaned device of the same model. To avoid time‐consuming maintenance of the sensors and to avoid additional sensors as much as possible, we developed a new method for measuring PV and radiometer soiling losses. The method makes use of a characterized lamp that is protected from soiling by a collimator and that illuminates the pyranometer or reference cell each night for some time. Comparing the signals of one night to the signal obtained at a night shortly after the last cleaning of the sensor, its soiling loss can be derived. To validate the measurements of soiling losses for the pyranometer and the reference cell, the soiling losses of the devices are also derived by comparing their signals to those of a clean sensor of the same type. These reference instruments are calibrated relative to the test devices so that deviations indicate the soiling loss of the test sensors. The first outdoor tests with 4 months of data show a good agreement with the reference measurements of the soiling losses. The accuracy of the method is estimated to be similar to that of the reference method, which involves the daily cleaning of the reference devices. [ABSTRACT FROM AUTHOR]

Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal resolution. The precision of PM mass concentration measurements from PMS 5003 sensors has been widely documented, though limited information is available regarding their size selectivity and number concentration measurement accuracy. In this work, PMS 5003 sensors, along with a Federal Referral Methods (FRM) sampler (Grimm spectrometer), were deployed across three sites with different atmospheric profiles, an urban (Germanou) and a background (UPat) site in Patras (Greece), and a semi-arid site in Almería (Spain, PSA). The LCSs particle number concentration measurements were investigated for different size bins. Findings for particles with diameter between 0.3 and 10 μm suggest that particle size significantly affected the LCSs' response. The LCSs could accurately detect number concentrations for particles smaller than 1 μm in the urban (R2 = 0.9) and background sites (R2 = 0.92), while a modest correlation was found with the reference instrument in the semi-arid area (R2 = 0.69). However, their performance was rather poor (R2 < 0.31) for coarser aerosol fractions at all sites. Moreover, during periods when coarse particles were dominant, i.e., dust events, PMS 5003 sensors were unable to report accurate number distributions (R2 values < 0.47) and systematically underestimated particle number concentrations. The results indicate that several questions arise concerning the sensors' capabilities to estimate PM2.5 and PM10 concentrations, since their size distribution did not agree with the reference instruments. [ABSTRACT FROM AUTHOR]

Numerical simulation applied to agriculture or wastewater treatment (WWT) is a complementary tool to understand, a priori, the impact of meteorological parameters on productivity under limiting environmental conditions or even to guide investments towards other more relevant circular economic objectives. This work proposes a new methodology to calculate Typical Meteorological Sequences (TMS) that could be used as input data to simulate the growth and productivity of photosynthetic organisms in different biological systems, such as a High-Rate Algae Pond (HRAP) for WWT or in agriculture for crops. The TMS was established by applying Finkelstein-Schafer statistics and represents the most likely meteorological sequence in the long term for each meteorological season. In our case study, 18 locations in the Madrid (Spain) region are estimated depending on climate conditions represented by solar irradiance and temperature. The parameters selected for generating TMS were photosynthetically active radiation, solar day length, maximum, minimum, mean, and temperature range. The selection of potential sequences according to the growth period of the organism is performed by resampling the available meteorological data, which, in this case study, increases the number of candidate sequences by 700%. [ABSTRACT FROM AUTHOR]

The aims of this work are to present an analysis of quality solar radiation data and develop several hourly models of photosynthetically active radiation (PAR) using combinations of radiometric variables such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) from their dimensionless indices atmospheric clearness index ( k t ), horizontal diffuse fraction ( k d ), and normal direct fraction ( k b ) together with solar elevation angle (α). GHI, DHI, and DNI data with 1-minute frequencies in the period from 2016 to 2021 from CEDER-CIEMAT, in a northern plateau, and PSA-CIEMAT in the southeast of the Iberian Peninsula, were used to compare two locations with very different climates according to the Köppen—Geiger classification. A total of 15 multilinear models were fitted and validated (with independent training and validation data) using first the whole dataset and then by k t intervals. In most cases, models including the clearness index showed better performance, and among them, models that also use the solar elevation angle as a variable obtained remarkable results. Additionally, according to the statistical validation, these models presented good results when they were compared with models in the bibliography. Finally, the model validation statistics indicate a better performance of the interval models than the complete models. [ABSTRACT FROM AUTHOR]

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (<10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.solener.2005.03.004 Byline: Jesus Polo, Luis F. Zarzalejo, Lourdes Ramirez, Bella Espinar Keywords: Solar irradiance; Meteosat satellite; Active learning; Ground database quality Abstract: A new technique of filtering solar radiation ground data is proposed for generating models for solar irradiance estimation from geostationary satellite data. The filtering processes consists of an iterative way of selecting the training data set to achieve the best model response. Although in this paper the proposed methodology has been used for solar irradiance modeling, it could be applied to any kind of empirical modeling. The iterative filtering method has proven to have fast convergence and to improve successfully the statistical model response, when applied to hourly global irradiance calculation from satellite-derived irradiances for 13 Spanish locations. Individual statistical models for hourly global irradiance were fitted using the Heliosat I method applied to Meteosat images of 13 Spanish stations for the period 1994-1996. Author Affiliation: Renewable Energy Department, CIEMAT, Avad. Complutense, 22, 28040 Madrid, Spain Article History: Received 26 January 2004; Revised 17 February 2005; Accepted 9 March 2005 Article Note: (miscellaneous) Communicated by: Associate Editor Pierre Ineichen

The share of distributed solar power generation is continuously growing. This increase, combined with the intermittent nature of the solar resource creates new challenges for all relevant stakeholders, from generation to transmission and demand. Insufficient consideration of intra‐minute and intra‐hour variabilities might lead to grid instabilities. Therefore, the relevance of nowcasts (shortest‐term forecasts) is steadily increasing. Nowcasts are suitable for fine‐grained control applications to operate solar power plants in a grid‐friendly way and to secure stable operations of electrical grids. In space and time, highly resolved nowcasts can be obtained by all sky imager (ASI) systems. ASI systems create hemispherical sky images. The associated software analyzes the sky conditions and derives solar irradiance nowcasts. Accuracy is the decisive factor for the effective use of nowcasts. Therefore, the goal of this work is to increase the nowcast accuracy by combining ASI nowcasts and persistence nowcasts, which persist with the prevailing irradiance conditions, while maintaining the spatial coverage and resolution obtained by the ASI system. This hybrid approach combines the strengths while reducing the respective weaknesses of both approaches. Results of a validation show reductions of the root mean square deviation of up to 12% due to the hybrid approach. [ABSTRACT FROM AUTHOR]

Solar resource assessment is of paramount importance in the planning of solar energy applications. Solar resources are abundant and characterization is essential for the optimal design of a system. Solar energy is estimated, indirectly, by the processing of satellite images. Several analyses with satellite images have considered a single variable—cloudiness. Other variables, such as albedo, have been recognized as critical for estimating solar irradiance. In this work, a multivariate analysis was carried out, taking into account four variables: cloudy sky index, albedo, linke turbidity factor (TL2), and altitude in satellite image channels. To reduce the dimensionality of the database (satellite images), a principal component analysis (PCA) was done. To determine regions with a degree of homogeneity of solar irradiance, a cluster analysis with unsupervised learning was performed, and two clustering techniques were compared: k-means and Gaussian mixture models (GMMs). With respect to k-means, the GMM method obtained a smaller number of regions with a similar degree of homogeneity. The multivariate analysis was performed in Mexico, a country with an extended territory with multiple geographical conditions and great climatic complexity. The optimal number of regions was 17. These regions were compared for annual average values of daily irradiation data from ground stations using multiple linear regression. A comparison between the mean of each region and the ground station measurement showed a linear relationship with a R 2 score of 0.87. The multiple linear regression showed that the regions were strongly related to solar irradiance. The optimal sites found are shown on a map of Mexico. [ABSTRACT FROM AUTHOR]

Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology-, climatology- and solar-energy-related applications. Since the shape and appearance of clouds is variable, and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy and cloud-free pixels without taking into account the cloud type. On the other hand, cloud classification is typically determined separately at the image level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks; however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit many more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300 000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky or a low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights using the same training and validation sets. Achieving 85.8 % pixel accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) of the respective cloud classes, where the improvement is between 5 and 20 percentage points. Furthermore, we compare the performance of our best model with regards to binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 percentage points, reaching a pixel accuracy of 95 %. [ABSTRACT FROM AUTHOR]

Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology, climatology and solar energy-related applications. Since the shape and appearance of clouds is variable and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy- and cloudfree-pixels, without taking into account the cloud type. On the other hand, cloud classification is typically determined separately on image-level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks, however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit much more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300,000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky, low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights, using the same training and validation sets. Achieving 85.8 % pixel-accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) on the respective cloud classes, where the improvement is between 5 and 20 % points. Furthermore, we compare the performance of our best model on binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 % points, reaching a pixel-accuracy of 95 %. [ABSTRACT FROM AUTHOR]

We present a validation study of direct normal irradiance (DNI) estimates from HelioMont with ground-based measurements from two European sites for the year 2015. The HelioMont algorithm infers irradiance with data from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument as the primary source of information on clouds, and data from models or reanalysis for other influential input parameters. The validation sites are the Plataforma Solar de Almería (PSA), a solar power research facility in Southern Spain characterized by arid conditions and the Swiss Baseline Surface Radiation Network (BSRN) site of Payerne, characterized by a much more frequent cloud coverage. Our analysis shows the importance of separately evaluating the quality of (1) the clear-sky irradiance computation and (2) the determination of the cloud effect. We also specifically investigate the cloud modification factor (CMF) using a validation CMF derived from ground-based data, giving us more insight into event-by-event agreement between HelioMont estimates and measured irradiances. The clear-sky HelioMont DNI uncertainty is mainly influenced by the aerosol optical depth (AOD) input data. Using the original AOD input (a 2008 climatology based on data from the Aerosol Comparisons between Observations and Models project) leads to large negative biases of 115 W m−2 to 145 W m−2. Using AOD from the Copernicus Atmosphere Monitoring Service (CAMS) allows reducing these biases to 15 W m−2 to 25 W m−2 (2% to 3%) with a dispersion of ±12% to ±15%, which is the HelioMont clear-sky DNI expanded uncertainty when using CAMS AOD. Using ground-measured AOD reduces this uncertainty to ±5% to ±6.5%, which is probably the limit of what is achievable with HelioMont. For all-sky comparisons, mean biases were between about −5 W m−2 and 55 W m−2 (depending on AOD input and station), while the root-mean-square deviation (RMSD) was between about 175 W m−2 and 195 W m−2. Our validation method yielded correlation between HelioMont and validation CMF between 0.79 and 0.92 (Pearson's correlation coefficient r), while RMSD was between 0.18 and 0.24. The computation of the cloud effect is the part of HelioMont that is the main source of uncertainty. Systematic errors were identified (underestimation of the number of near-zero DNI and overestimation of the number of clear-sky cases) and solving them may lead to substantial improvement. [ABSTRACT FROM AUTHOR]

Abstract: Because of the cloud‐induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward‐facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial‐sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all‐sky cameras (“WobaS‐4cam”). The WobaS‐4cam system is operational at 2 solar energy research centers and at a commercial 50‐MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS‐4cam system is considered to be at least state of the art. [ABSTRACT FROM AUTHOR]

Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented. [ABSTRACT FROM AUTHOR]

This work addresses the development of a PAR model in the entire territory of mainland Spain. Thus, a specific model is developed for each location of the study field. The new PAR model consists of a combination of the estimates of two previous models that had unequal performances in different climates. In fact, one of them showed better results with Mediterranean climate, whereas the other obtained better results under oceanic climate. Interestingly, the new PAR model showed similar performance when validated at seven stations in mainland Spain with Mediterranean or oceanic climate. Furthermore, all validation slopes ranged from 0.99 to 1.00; the intercepts were less than 3.70 μmol m−2 s−1; the R2 were greater than 0.988, while MBE was closer to zero percent than −0.39%; and RMSE were less than 6.21%. The estimates of the PAR model introduced in this work were then used to develop PAR maps over mainland Spain that represent daily PAR averages of each month and a full year at all locations in the study field. [ABSTRACT FROM AUTHOR]

Utility-scale hybrid wind–solar PV power plants (which might include some storage as well) are an attractive option for the transition of conventional grids to incorporate high renewable energy (RE) shares. Along with lower generation costs, they offer increased dispatch capabilities and flexible operation. However, when assessing their potential, they present higher needs in terms of input data, as they are forced to consider both spatial and temporal variations to evaluate their techno-economic viability, as well as other common inputs such as economic, social or environmental data. The availability of the different data influences the type of analysis to be implemented. The microgrid approach of segmenting the information into layers will be adopted for the classification of data. Three different levels of analysis are sought: long-term energy scenarios, geo-spatial planning, and production cost estimation. The analysis of necessary data for each planning stage, and the available data sources for the assessment of utility-scale hybrid power plants, under this microgrid approach, is the main focus of this review. [ABSTRACT FROM AUTHOR]

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature ( T a i r ), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest. [ABSTRACT FROM AUTHOR]

In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol particles and water vapor. Various on-site measurement methods of atmospheric extinction in solar tower plants have been developed during recent years, but during resource assessment for distinct tower plant projects in-situ measurement data sets are typically not available. To overcome this lack of information, a transmittance model (TM) has been previously developed and enhanced by the authors to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity and barometric pressure measurements. Previously the model was only tested at one site. In this manuscript, the enhanced TM is validated for three sites (CIEMAT's Plataforma Solar de Almería (PSA), Spain, Missour, Morocco (MIS) and Zagora, Morocco (ZAG)). As the strongest assumption in the TM is the vertical aerosol particle profile, three different approaches to describe the vertical profile are tested in the TM. One approach assumes a homogeneous aerosol profile up to 1 kilometer above ground, the second approach is based on LIVAS profiles obtained from Lidar measurements and the third approach uses boundary layer height (BLH) data of the European Centre for Medium-Range Weather Forecasts (ECMWF). The derived broadband transmittance for a slant range of 1 km ( T 1 k m ) time series is compared with a reference data set of on-site absorption- and broadband corrected T 1 k m derived from meteorological optical range (MOR) measurements for the temporal period between January 2015 and November 2017. The absolute mean bias error (MBE) for the TM's T 1 k m using the three different aerosol profiles lies below 5% except for ZAG and one profile assumption. The MBE is close to 0 for PSA and MIS assuming a homogeneous extinction coefficient up to 1 km above ground. The root mean square error (RMSE) is around 5–6% for PSA and ZAG and around 7–8% for MIS. The TM performs better during summer months, during which more data points have been evaluated. This validation proves the applicability of the transmittance model for resource assessment at various sites. It enables the identification of a clear site with high T 1 k m with a high accuracy and provides an estimation of the T 1 k m for hazy sites. Thus it facilitates the decision if on-site extinction measurements are necessary. The model can be used to improve the accuracy of yield analysis of tower plants and allows the site adapted design. [ABSTRACT FROM AUTHOR]

The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts. [ABSTRACT FROM AUTHOR]

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