106 results on '"Zarzalejo, Luis"'
Search Results
2. Benchmarking of solar irradiance nowcast performance derived from all-sky imagers
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Logothetis, Stavros-Andreas, Salamalikis, Vasileios, Wilbert, Stefan, Remund, Jan, Zarzalejo, Luis F., Xie, Yu, Nouri, Bijan, Ntavelis, Evangelos, Nou, Julien, Hendrikx, Niels, Visser, Lennard, Sengupta, Manajit, Pó, Mário, Chauvin, Remi, Grieu, Stephane, Blum, Niklas, van Sark, Wilfried, and Kazantzidis, Andreas
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- 2022
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3. Parabolic trough field control utilizing all sky imager irradiance data – A comprehensive robustness analysis
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Kotzab, Tim, Müllner, Sebastian, Hirsch, Tobias, Noureldin, Kareem, Nouri, Bijan, Schmitz, Mark, Zarzalejo, Luis Fernando, and Pitz-Paal, Robert
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- 2022
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4. Optimizing Solar Potential Analysis in Cuba: A Methodology for High-Resolution Regional Mapping.
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Domínguez, Javier, Bellini, Carlo, Martín, Ana María, and Zarzalejo, Luis F.
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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]
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- 2024
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5. Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager
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Blum, Niklas Benedikt, Wilbert, Stefan, Nouri, Bijan, Lezaca, Jorge, Huckebrink, David, Kazantzidis, Andreas, Heinemann, Detlev, Zarzalejo, Luis F., Jiménez, María José, and Pitz-Paal, Robert
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- 2022
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6. Combining Deep Learning and Physical Models: A Benchmark Study on All‐Sky Imager‐Based Solar Nowcasting Systems.
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Fabel, Yann, Nouri, Bijan, Wilbert, Stefan, Blum, Niklas, Schnaus, Dominik, Triebel, Rudolph, Zarzalejo, Luis F., Ugedo, Enrique, Kowalski, Julia, and Pitz‐Paal, Robert
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HYBRID systems ,SOLAR system ,DEEP learning ,ENERGY infrastructure ,SOLAR energy ,SPATIAL resolution - Abstract
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]
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- 2024
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7. Probabilistic assessment of concentrated solar power plants yield: The EVA methodology
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Fernández-Peruchena, Carlos M., Vignola, Frank, Gastón, Martín, Lara-Fanego, Vicente, Ramírez, Lourdes, Zarzalejo, Luis, Silva, Manuel, Pavón, Manuel, Moreno, Sara, Bermejo, Diego, Pulgar, Jesús, Macias, Sergio, and Valenzuela, Rita X.
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- 2018
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8. Autonomous measurement system for photovoltaic and radiometer soiling losses.
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Campos, Laura, Wilbert, Stefan, Carballo, José, Meyer zu Köcker, Juliane, Wolfertstetter, Fabian, La Casa, José, Borg, Erik, Schmidt, Karsten, Zarzalejo, Luis F., Fernández‐García, Aránzazu, Santos, Fernanda Norde, and García, Ginés
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SOIL erosion ,PHOTOVOLTAIC power systems ,RADIOMETERS ,PYRANOMETER ,FALSE alarms - Abstract
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]
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- 2023
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9. Temporal variability patterns in solar radiation estimations
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Vindel, José M., Navarro, Ana A., Valenzuela, Rita X., and Zarzalejo, Luis F.
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- 2016
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10. A statistical characterization of the long-term solar resource: Towards risk assessment for solar power projects
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Fernández Peruchena, Carlos M., Ramírez, Lourdes, Silva-Pérez, Manuel A., Lara, Vicente, Bermejo, Diego, Gastón, Martín, Moreno-Tejera, Sara, Pulgar, Jesús, Liria, Juan, Macías, Sergio, Gonzalez, Rocío, Bernardos, Ana, Castillo, Nuria, Bolinaga, Beatriz, Valenzuela, Rita X., and Zarzalejo, Luis F.
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- 2016
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11. Una nueva estrategia integral para modelar materiales urbanos para ciudades térmicamente habitables. Un caso de estudio en Madrid = A new integrated strategy for modelling urban materials for thermally liveable cities. A case study in Madrid
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Giancola, Emanuela, López, Helena, Soutullo, Silvia, Sánchez, M. Nuria, Zarzalejo, Luis F., Gamarra, Ana, Herrera, Israel, Ferrer, J. Antonio, and Naboni, Emanuele
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Diseño Paramétrico ,Herramienta de Simulación Exterior ,Materiales urbanos optimizados ,Isla de Calor Urbana ,Cambio Climático ,Parametric Design ,Exterior Simulation Tool ,Optimised Urban Materials ,Urban Heat Island ,Climate Change - Abstract
Desde el punto de vista térmico, el uso de materiales multifuncionales e innovadores en las superficies urbanas puede proporcionar mejoras radicales y reducir el efecto de Isla de Calor Urbana. En la simulación energética de edificios se ha prestado poca atención a las interacciones, no despreciables, entre la envolvente exterior de los edificios, la demanda energética y su impacto en el microclima urbano. Las herramientas actuales de simulación de edificios tienen una capacidad limitada para evaluar estas interrelaciones. Por estas razones, es necesario crear flujos de trabajo basados en simulaciones ad-hoc capaces de evaluar la influencia de los materiales y sus impactos en ambientes exteriores e interiores. Este artículo muestra la estrategia de simulación que se utilizará en un proyecto de investigación nacional cuyo objetivo es validar la viabilidad del uso de materiales urbanos optimizados, mediante simulación. Para ello se utilizará un innovador flujo de trabajo racionalizado basado en la herramienta Grasshopper.AbstractFrom a thermal point of view, the use of multifunctional and innovative materials in urban surfaces can provide radical improvements and reduce the Urban Heat Island effect. In building energy simulation, little attention has been paid to the non-negligible interactions between the external building envelope, energy demand and its impact on the urban microclimate. Current building simulation tools are limited in their ability to assess these interrelationships. For these reasons, it is necessary to create workflows based on ad-hoc simulations capable of assessing the influence of materials and their impacts on outdoor and indoor environments. This article shows the simulation strategy to be used in a national research project aiming to validate the feasibility of using optimised urban materials through simulation. An innovative streamlined workflow based on the Grasshopper tool will be used for this purpose.
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- 2022
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12. Soiling Persistence Model as Benchmark for Soiling Forecasts of Solar Collectors
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Norde Santos, Fernanda, Hanrieder, Natalie, Wilbert, Stefan, Polo, Jesús, Alonso García, Carmen, Zarzalejo, Luis F., Abraim, Mounir, Abdellatif, Ghennioui, and Pitz-Paal, Robert
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persistence model ,benchmark model ,soiling ,soiling forecast - Published
- 2022
13. Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain.
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Kosmopoulos, Georgios, Salamalikis, Vasileios, Wilbert, Stefan, Zarzalejo, Luis F., Hanrieder, Natalie, Karatzas, Stylianos, and Kazantzidis, Andreas
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WEATHER ,DETECTORS ,PARTICULATE matter ,MASS measurement - Abstract
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 (R
2 = 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]- Published
- 2023
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14. Generation of Typical Meteorological Sequences to Simulate Growth and Production of Biological Systems.
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Wane, Ousmane, Zarzalejo, Luis F., Ferrera-Cobos, Francisco, Navarro, Ana A., Rodríguez-López, Alberto, and Valenzuela, Rita X.
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BIOLOGICAL systems ,WASTEWATER treatment ,SOLAR temperature ,CROP growth ,PONDS ,COMPUTER simulation - Abstract
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]
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- 2023
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15. Comfort Evaluation in an Urban Boulevard by Means of Evaporative Wind Towers
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Castro, Silvia Soutullo, Guaita, Cristina Sanjuan, Egido, Maria Nuria Sánchez, Zarzalejo, Luis F., Miranda, Ricardo Enríquez, and Celemín, Maria del Rosario Heras
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- 2012
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16. SISTEMA INTEGRADO DE CONTROL PARA EL ABASTECIMIENTO DE ENERGÍA MEDIANTE SISTEMAS HÍBRIDOS EN COMUNIDADES AISLADAS DE CUBA. FASE II (PROYECTO HIBRI2)
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Dominguez, Javier, Arribas, Luis, De Diego, Lara, Herrera, Israel, and Zarzalejo, Luis F.
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- 2022
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17. Solar Irradiance Ramp Forecasting Based on All-Sky Imagers
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Logothetis, Stavros-Andreas, Salamalikis, Vasileios, Nouri, Bijan, Remund, Jan, Zarzalejo, Luis F., Xie, Yu, Wilbert, Stefan, Ntavelis, Evangelos, Nou, Julien, Hendrikx, Niels, Visser, Lennard, Sengupta, Manajit, Pó, Mário, Chauvin, Remi, Grieu, Stéphane, Blum, Niklas, Sark, Wilfried van, Kazantzidis, Andreas, Integration of Photovoltaic Solar Energy, Energy and Resources, Integration of Photovoltaic Solar Energy, and Energy and Resources
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Control and Optimization ,Renewable Energy, Sustainability and the Environment ,All sky imager ,Energy Engineering and Power Technology ,ramp events ,forecasting ,Building and Construction ,Ramp rate ,Benchmark ,solar irradiance ramp event forecasting ,all-sky imagers ,Solar irradiance nowcasts ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,Energy (miscellaneous) - Abstract
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 (
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- 2022
18. Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning
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Martín, Luis, Zarzalejo, Luis F., Polo, Jesús, Navarro, Ana, Marchante, Ruth, and Cony, Marco
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- 2010
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19. A new statistical approach for deriving global solar radiation from satellite images
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Zarzalejo, Luis F., Polo, Jesús, Martín, Luis, Ramírez, Lourdes, and Espinar, Bella
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- 2009
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20. Angstrom turbidity and ozone column estimations from spectral solar irradiance in a semi-desertic environment in Spain
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Polo, Jesús, Zarzalejo, Luis F., Salvador, Pablo, and Ramírez, Lourdes
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- 2009
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21. Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations
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Espinar, Bella, Ramírez, Lourdes, Drews, Anja, Beyer, Hans Georg, Zarzalejo, Luis F., Polo, Jesús, and Martín, Luis
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- 2009
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22. Comparative Analysis of Photosynthetically Active Radiation Models Based on Radiometric Attributes in Mainland Spain.
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Wane, Ousmane, Ramírez Ceballos, Julián A., Ferrera-Cobos, Francisco, Navarro, Ana A., Valenzuela, Rita X., and Zarzalejo, Luis F.
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SOLAR radiation ,COMPARATIVE studies ,RADIATION ,MODEL validation - Abstract
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]
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- 2022
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23. Irradiance maps from a shadow camera on a mountain range.
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Wilbert, Stefan, Nouri, Bijan, Kötter-Orthaus, Norbert, Hanrieder, Natalie, Prahl, Christoph, Kuhn, Pascal, Zarzalejo, Luis, and Lázaro, Roberto
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SOLAR power plants ,CAMERAS ,SOLAR energy ,MOUNTAINS - Abstract
Spatially and temporally highly resolved direct normal irradiance (DNI) maps of the solar field can be used to significantly improve the operation of concentrating solar power (CSP) plants. Further improvements are expected if DNI nowcasts are also used for the plant operation. Currently, the most widely used method to create spatially resolved DNI nowcasts is based on all sky imagers. All sky imagers take photos of the whole sky above the CSP plant and detect clouds to derive the DNI map. Such all sky imager systems are promising, but for the calculation of DNI maps the cloud height and shape must be determined. Depending on the conditions, these processing steps can be error prone. Shadow cameras avoid these complex tasks. Mounted on an elevated position, they take photos of the ground in which cloud shadows can be detected. The RGB values of the photos and one measurement station in the camera's field of view are used to derive the DNI for each pixel of the photo. So far, a shadow camera system was mounted on a solar tower, covering an area of about 4 km². However, mirror surfaces and ground shaded by structures seen in the images cannot be evaluated with the method. Together with the low spatial extension, this so far excluded the shadow camera approach from the application in utility scale CSP plants. To overcome these limitations, a shadow camera is mounted on a mountain range. The evaluation method is adapted to the large distances between the camera and the ground and the results are compared to ground based DNI measurements. Considering that a simple surveillance camera is used the results are satisfying. The method is promising for tower plants and other solar power plants located close to mountain ranges or high buildings and can also help to provide highly resolved forecasts of global irradiance for areas with distributed solar energy systems. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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24. Implementation of the heliosat 2 model in Mexico from GOES 13 satellités images.
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Riveros-Rosas, David, Sánchez-Diaz, María. E, Velasco-Herrera, Víctor M., González-Cabrera, Adriana E., Valdés-Barrón, Mauro, and Zarzalejo, Luis F.
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REMOTE-sensing images ,INTERPOLATION - Abstract
The use of satellite images for local evaluation of the solar resource represents a more accurate method than interpolation of surface data measured in broad regions (Rigollier et al. 2004). This work presents the first results of the implementation and adequacy of the Heliosat 2 model, for use with images of the GOES13 satellite (Rigollier et al. 2004) (Albarelo et al. 2015) for the case of Mexico. With the model, the results of daily radiation averages monthly were obtained for a full annual cycle. The results also show an average annual relative error, between modeled and measured data, acceptable and comparable with previous works in other regions in the world. [ABSTRACT FROM AUTHOR]
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- 2022
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25. Stowing strategy for a heliostat field based on wind speed and direction.
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Emes, Matthew, Jafari, Azadeh, Collins, Mike, Wilbert, Stefan, Zarzalejo, Luis, Siegrist, Silvan, and Arjomandi, Maziar
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WIND speed ,HELIOSTATS ,AZIMUTH ,WIND pressure - Abstract
This paper investigates a stowing strategy of a heliostat field based on wind speed and direction, in terms of the potential benefit of additional energy collection through the partial stowing of heliostats within an azimuth angle range with reduced operating wind loads. Correlations of one-minute wind speed and DNI at a heliostat field site with the operating wind loads, based on the azimuth-elevation tracking angles of individual heliostats, were used to assess the increased operating time and collected thermal energy by the field. The results show that more than 23% of heliostats in the sector of the field with operating wind loads that are smaller than 50% of the stow loads can continue to operate during a high-wind period (e.g. 10 m/s). Adopting a stow strategy based on wind direction can increase the annual operating time of the heliostat field by 6% with increasing stow design wind speed from 6 m/s to 12 m/s. Furthermore, the stowing strategy based on wind direction to allow heliostats to continue to operate at wind speeds exceeding 10 m/s can achieve an additional 280 MWh of thermal energy collected by the heliostat field operation during time periods that would conventionally stow the entire field with 24 GWh of annual thermal energy captured. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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26. A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy.
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Nouri, Bijan, Blum, Niklas, Wilbert, Stefan, and Zarzalejo, Luis F.
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DISTRIBUTED power generation ,SOLAR power plants ,ELECTRIC power distribution grids ,ROOT-mean-squares ,SOLAR technology ,SPATIAL resolution - Abstract
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]
- Published
- 2022
- Full Text
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27. Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite.
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Salinas-González, Jared D., García-Hernández, Alejandra, Riveros-Rosas, David, Moreno-Chávez, Gamaliel, Zarzalejo, Luis F., Alonso-Montesinos, Joaquín, Galván-Tejada, Carlos E., Mauricio-González, Alejandro, and González-Cabrera, Adriana E.
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MULTIVARIATE analysis ,REMOTE-sensing images ,GAUSSIAN mixture models ,IMAGE analysis ,CLUSTER analysis (Statistics) ,K-means clustering - Abstract
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]
- Published
- 2022
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28. AATTENUATION-The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation
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Hanrieder, Natalie, Ghennioui, Abdellatif, Wilbert, Stefan, Sengupta, Manajit, and Zarzalejo, Luis F.
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attenuation loss ,CSP ,lcsh:T ,transmittance model ,solar energy ,central receiver ,solar resource assessment ,Qualifizierung ,lcsh:Technology ,atmospheric extinction ,solar tower plant - Abstract
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 (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.
- Published
- 2020
29. Iterative filtering of ground data for qualifying statistical models for solar irradiance estimation from satellite data
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Polo, Jesus, Zarzalejo, Luis F., Ramirez, Lourdes, and Espinar, Bella
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Artificial satellites -- Analysis ,Artificial satellites -- Models ,Radiation -- Analysis ,Radiation -- Models ,Satellite communications services industry -- Analysis ,Satellite communications services industry -- Models ,Alternative energy sources -- Analysis ,Alternative energy sources -- Models ,Earth sciences ,Petroleum, energy and mining industries - Abstract
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
- Published
- 2006
30. Applying self-supervised learning for semantic cloud segmentation of all-sky images.
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Fabel, Yann, Nouri, Bijan, Wilbert, Stefan, Blum, Niklas, Triebel, Rudolph, Hasenbalg, Marcel, Kuhn, Pascal, Zarzalejo, Luis F., and Pitz-Paal, Robert
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SUPERVISED learning ,IMAGE reconstruction ,VISUAL learning ,IMAGE segmentation ,PIXELS ,ARTIFICIAL neural networks - Abstract
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]
- Published
- 2022
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- View/download PDF
31. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index
- Author
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Zarzalejo, Luis F., Ramirez, Lourdes, and Polo, Jesus
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- 2005
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32. Evaluation of an all sky imager based nowcasting system for distinct conditions and five sites.
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Nouri, Bijan, Wilbert, Stefan, Blum, Niklas, Kuhn, Pascal, Schmidt, Thomas, Yasser, Zeyad, Zarzalejo, Luis F., Lopes, Francisco M., Silva, Hugo G., Schroedter-Homscheidt, Marion, Kazantzidis, Andreas, Raeder, Christian, Blanc, Philippe, Pitz-Paal, Robert, and Richter, Christoph
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SOLAR power plants ,ELECTRIC power distribution grids ,SKY ,WEATHER ,COMPLEX variables - Abstract
All sky imager (ASI) based nowcasting system can provide spatially and temporally highly resolved solar irradiance information for the next minutes ahead. Nowcasts, which capture the intra-hour variability of the incoming downward shortwave solar irradiance, have the potential to optimize the operation of solar power plants as well as electrical grids. Such automatized optimizations require a deep understanding of the accuracy in the nowcasting system at any given moment. State of the art validation procedures of ASI based nowcasting systems use scalar error metrics without regards to the actual weather conditions. Yet, the performance of nowcasting systems varies strongly with the prevailing weather conditions. Deviations increase for more complex and variable conditions, for which it is more challenging to detect and model the clouds in the sky. Thus, depending on the used data set such validation results may not be meaningful to describe the expected accuracy in realistic and individual optimization situations. A novel validation procedure is presented in this work, which discretizes the validation data set in distinct temporal DNI variability classes. Individual error metrics are determined as function of the lead time and DNI variability class. This approach is applied for a two ASI based nowcasting system as operated on five distinct sites distributed in Spain, Portugal and Germany, over a combined period of more than 4.5 years. The obtained validation results emphasize that the novel classification method enables a comparison in nowcast performance between the sites despite of distinct local meteorological conditions. The presented method allows the estimation of the overall accuracy of nowcasting systems at a new site if DNI data in 1 min resolution are available. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
33. A way to increase parabolic trough plant yield by roughly 2% using all sky imager derived DNI maps.
- Author
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Nouri, Bijan, Noureldin, Kareem, Schlichting, Tim, Wilbert, Stefan, Hirsch, Tobias, Schroedter-Homscheidt, Marion, Kuhn, Pascal, Kazantzidis, Andreas, Zarzalejo, Luis F., Blanc, Philippe, Yasser, Zeyad, Fernández, Jesús, Pitz-Paal, Robert, and Richter, Christoph
- Subjects
PARABOLIC troughs ,PLANT yields ,SOLAR power plants ,MAXIMUM power point trackers ,SKY ,POINT set theory - Abstract
Solar fields of parabolic troughs are extensive complex thermal hydraulic facilities. Intra-hour and intra-minute variabilities of the DNI, mainly caused by passing clouds, pose an operational challenge for parabolic trough power plants. Under perfect circumstances a solar field controller would adjusts the mass flow in such a way, that the design temperature is always maintained constant with a maximized focus rate. However, heterogeneous irradiance conditions or flow distribution may cause some solar field sections to temporarily overheat while others may not reach the set point temperature, which in turn leads to an economic loss. State of the art solar field controllers have only access to incomplete information on spatial DNI variability, from DNI measurements of few pyrheliometers. Solar field controllers could be optimized with access to highly resolved DNI informations both in space and time. Such DNI information can be provided by all sky imager (ASI) based irradiance monitoring systems. In a previous study we developed and benchmarked new solar field controllers with access to spatial DNI information from an ASI system for a 50 MWe plant close to Córdoba (Spain). Significant improvements in revenue were observed. Yet, this previous study was limited to 22 days only. In this study, we estimate the potential benefit of these new solar field controllers over a 2 year period on the basis of the simulation results over 22 days. The upscaling method makes use of DNI variability classes. Using the ASI data we obtain a significant improvement in revenue up to 2% for the 2 year period. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
34. Applying self-supervised learning for semantic cloud segmentation of all-sky images.
- Author
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Fabel, Yann, Nouri, Bijan, Wilbert, Stefan, Blum, Niklas, Triebel, Rudolph, Hasenbalg, Marcel, Kuhn, Pascal, Zarzalejo, Luis F., and Pitz-Paal, Robert
- Subjects
IMAGE segmentation ,VISUAL learning ,IMAGE reconstruction ,SUPERVISED learning ,ARTIFICIAL neural networks ,CLIMATOLOGY - Abstract
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]
- Published
- 2021
- Full Text
- View/download PDF
35. Accuracy of satellite-derived solar direct irradiance in Southern Spain and Switzerland.
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Vuilleumier, Laurent, Meyer, Angela, Stöckli, Reto, Wilbert, Stefan, and Zarzalejo, Luis F.
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SOLAR spectra ,WEATHER control ,STANDARD deviations ,PEARSON correlation (Statistics) ,OPTICAL depth (Astrophysics) ,SOLAR energy - Abstract
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]- Published
- 2020
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- View/download PDF
36. Optimized DNI forecast using combinations of nowcasting methods from the DNICast project
- Author
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Ramírez, Lourdes, Hanrieder, Natalie, Zarzalejo, Luis, Landelius, Tomas, Müller, Stefan, Schroedter Homscheidt, Marion, Wilbert, Stefan, Dubranna, Jean, Remund, Jan, Vindel, Jose Maria, Valenzuela, Rita, Kuhn, Pascal, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas [Madrid] (CIEMAT), German Remote Sensing Data Center (DLR-DFD), German Aerospace Center (DLR), Centre Observation, Impacts, Énergie (O.I.E.), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), and Meteotest
- Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology ,[SPI.NRJ]Engineering Sciences [physics]/Electric power - Abstract
International audience; Introduction The efficient operation of concentrating solar technologies (CST) requires reliable forecasts of the Direct Normal Irradiance (DNI) for two main reasons: a better management of the thermodynamic cycle and the optimization of electricity produced/supplied to the grid. Within the EU FP7 project DNICast (Direct Normal Irradiance Nowcasting methods for the optimized operation of concentrating solar technologies), a set of innovative and/or improved forecast methods are proposed. Three different methodologies with different nowcast windows have been developed and combined to improve the nowcasting outputs:-DNI nowcasting method with all sky imagers for the next 0 to 15 min (part of the intra-hour nowcasting window according to the definition used in electricity grid operations).-Satellite based cloud and DNI nowcasting methods: intra-hour and intraday window, 5 min – 360 min.-Numerical Weather Prediction (NWP) based nowcasting methods: intraday and intraweek window, 60 min – 360 min and more.Combination methodologies and resultsTwo combination approaches have been tested. We have selected different nowcasting outputs from the DNICastproject which have been tested at PSA. Data of 12 months have been analyzed.The first approach makes use of the uncertainty of each input nowcasting data set. To calculate the optimal combinationof the different provided nowcasts, the method of is applied to derive a combined product, considering theindividual uncertainties of the nowcast products.The second combination method uses a time-dependent multi-regressive model inspired by the forecastingoptimization of precipitation. An adaptive linear merging model is presented. The explanatory variables areDNI values predicted in previous forecast events. Thus, each DNICast nowcasting output provides a number ofvariables depending on the forecasted horizon, the refresh time and the time step.In the full paper, the models and the validation of the combined models with pyrheliometer measurements of DNIin comparison to each individual nowcast for several periods from 2010 to 2015 will be presented. It was foundthat the RMSE of the combined nowcasts lies significantly below the RMSE of the single nowcast methods. Forexample for the uncertainty based nowcast combination a reduction from 305-339 W/m2 of the different methodsto 272 W/m2 was found for June to August 2015. In general, combined model underestimates the variability whenthe optimized model overestimates it.
- Published
- 2017
37. Modelling the Soiling Rate: Dependencies on Meteorological Parameters.
- Author
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Wolfertstetter, Fabian, Wilbert, Stefan, Terhag, Felix, Hanrieder, Natalie, Fernandez-García, Aranzazu, Sansom, Christopher, King, Peter, Zarzalejo, Luis, and Ghennioui, Abdellatif
- Subjects
CHLOROPHYLL spectra ,FACTORY design & construction ,PLANT selection ,SOLAR energy ,WIND speed ,HUMIDITY - Abstract
Concentrating solar power (CSP) plants are often located in dusty environments. Soiling depends strongly on location, time, weather conditions and mirror orientation and is characterized by the soiling rate: the loss of the specular reflectance due to soiling per time interval. The average soiling rate can reach 2%/day on sites with heavy dust loads such as the Arabian Peninsula. On some days (for example during a sandstorm) the soiling rate can be significantly higher than that. Measurement campaigns for the soiling rate are of interest for the CSP plant site selection and the plant design, but they are time consuming and costly. In this study, a soiling model is presented that describes particle deposition processes based on physical equations from where the soiling rate can be derived. The model uses easily measureable meteorological parameters such as aerosol particle number concentration, wind speed and direction at 10 m height, relative humidity, temperature and precipitation as input parameters. The model has been optimized and validated using measurement data from two sites in Morocco and Spain. The measurement data have been divided into two parts. One was used to find optimum model parameters by parameterization. The second dataset was used to validate the model. The model reaches a bias of 0.1%/d and a root mean square deviation of 0.4 %/d. Days with weak soiling (<1%/d) rates are identified with an accuracy of more than 90 %, the question whether or not the soiling rate is above 1%/d is answered correctly in 85 % of the cases. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
38. Report on calibration campaigns for pyrheliometer and pyranometer SFERA II Project Dissemination Level Public
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Nouri, Bijan, Wilbert, Stefan, Ginés García, Ramírez, Lourdes, Zarzalejo, Luis, Valenzuela, Rita, Ferrera, Francisco, and Guillot, Emmanuel
- Published
- 2016
- Full Text
- View/download PDF
39. Physically based correction of systematic errors of Rotating Shadowband Irradiometers.
- Author
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FORSTINGER, ANNE, WILBERT, STEFAN, DRIESSE, ANTON, HANRIEDER, NATALIE, AFFOLTER, ROMAN, KUMAR, SHARAD, GOSWAMI, NEERAJ, GEUDER, NORBERT, VIGNOLA, FRANK, ZARZALEJO, LUIS, and HABTE, ARON
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ERROR correction (Information theory) ,SOLAR power plants ,WEATHER - Abstract
Accurate measurements of direct normal, diffuse horizontal and global horizontal irradiance (DNI, DHI and GHI) are needed for meteorological studies and are essential for the solar resource assessment at potential solar power plant sites. Often, these potential sites are remote and hence require robust sensors that require minimal maintenance that are not affected strongly by soiling. Therefore, Rotating Shadowband Irradiometers (RSI) are widely used for resource assessment. To achieve the required accuracy, corrections for the raw values of RSIs depending on systematic temperature, incidence angle and spectral errors must be used, and a thorough calibration of the sensor head must be applied. The existing correction functions are derived from comparisons of RSIs to thermopile radiometers at selected sites and therefore empirical. Their accuracy is considered to be site dependent. In this work a new correction and calibration method is presented that removes the systematic errors using a physical approach. It is based on information of the sensor properties as well as measurements of its directional response, and incorporates the atmospheric conditions at the measurement site. In this case, no empiric relations obtained from a specific site are required. The method requires estimates of the current DHI and GHI spectra during each measurement of the RSI. Based on these spectra, a spectral correction, which includes a spectrum dependent temperature correction, can be made without employing empirical relationships. The new physical calibration and correction method is tested at three sites and reaches similar results compared to the empirical functions. This is already achieved with rudimentary estimations of the GHI and DHI spectra and we expect that these estimations can be improved in the future. The results indicate that the physical approach reduces the problematic location dependence of the current calibration and correction methods. The physical correction and calibration method show promise for a further improvement of the RSI accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
40. Towards the Chilean Solar Thermal Potential Knowledge for Solar Power Tower Plants.
- Author
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Marzo, Aitor, Zarzalejo, Luis F., Ibarra, Mercedes, Navarro, Ana A., Soto, Gonzalo, Ramirez, Lourdes, Escobar, Rodrigo, and Silva-Pérez, Manuel
- Subjects
- *
SOLAR thermal energy , *SOLAR power plants , *SOLAR radiation , *HEAT storage , *SOLAR energy - Abstract
The present paper presents a very simple energy yield model fitted using the annual DNI and the latitude as main inputs, considering a solar tower CSP plant, with 100 MW of net energy output and 6 hours of thermal storage. Furthermore, a mask of suitable areas for CSP power tower installations in Chile is also shown. The mapping of solar radiation components has been calculated from multi-regressive models based on ground based measurements, existing maps of solar resources and atmospheric parameters. An analysis of the available data bases in Chile is also done in order to obtain useful information for the development of the work. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
41. Analysis of Linke Turbidity Index from Solar Measurements in Mexico.
- Author
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Riveros-Rosas, David, Gonzalez-Cabrera, Adriana E., Valdes-Barrón, Mauro G., Zarzalejo, Luis F., and Ramirez, Lourdes
- Subjects
MEASUREMENT of solar radiation ,METEOROLOGICAL stations ,TURBIDITY ,OPTICAL properties ,SPECTRAL irradiance - Abstract
The atmospheric Linke Turbidity index is determined from solar radiation measurements of global irradiance for different weather stations of México. The Linke turbidity index is obtained from ESRA clear sky model by adjusting the index value that gives the smallest difference between the model and the measured irradiances. Clear days were identified in the solar database of selected stations and the monthly mean value for the turbidity is compared with Linke values worldwide provided from Meteotest through their internet services. Significant differences are found in some cases; but in most cases, the measured variability shows agreement with the Meteotest values. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
42. Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiante Historical Data.
- Author
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Caballero, Rafael, Zarzalejo, Luis F., Otero, Álvaro, Piñuel, Luis, and Wilbert, Stefan
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SOLAR power plants ,SOLAR energy ,ARTIFICIAL neural networks - Abstract
Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2018
- Full Text
- View/download PDF
43. Validation of Spatially Resolved All Sky Imager Derived DNI Nowcasts.
- Author
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Kuhn, Pascal, Wilbert, Stefan, Schüler, David, Prahl, Christoph, Haase, Thomas, Ramirez, Lourdes, Zarzalejo, Luis, Meyer, Angela, Vuilleumier, Laurent, Blanc, Philippe, Dubrana, Jean, Kazantzidis, Andreas, Schroedter-Homscheidt, Marion, Hirsch, Tobias, and Pitz-Paal, Robert
- Subjects
REMOTE-sensing images ,SOLAR radiation ,SOLAR power plants ,PREDICTION models ,NOWCASTING (Meteorology) - Abstract
Mainly due to clouds, Direct Normal Irradiance (DNI) displays short-term local variabilities affecting the efficiency of concentrating solar power (CSP) plants. To enable efficient plant operation, DNI nowcasts in high spatial and temporal resolutions for 15 to 30 minutes ahead are required. Ground-based All Sky Imagers (ASI) can be used to detect, track and predict 3D positions of clouds possibly shading the plant. The accuracy and reliability of these ASI-derived DNI nowcasts must be known to allow its application in solar power plants. Within the framework of the European project DNICast, an ASI-based nowcasting system was developed and implemented at the Plataforma Solar de Almería (PSA). Its validation methodology and validation results are presented in this work. The nowcasting system outperforms persistence forecasts for volatile irradiance situations. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
44. A Methodology for Probabilistic Assessment of Solar Thermal Power Plants Yield.
- Author
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Fernández-Peruchena, Carlos M., Lara-Faneho, Vicente, Ramírez, Lourdes, Zarzalejo, Luis F., Silva, Manuel, Bermejo, Diego, Gastón, Martín, Moreno, Sara, Pulgar, Jesús, Pavon, Manuel, Macías, Sergio, and Valenzuela, Rita X.
- Subjects
SOLAR thermal energy ,SOLAR power plants ,ELECTRIC power production ,ENERGY transfer ,IRRADIATION ,SOLAR temperature - Abstract
A detailed knowledge of the solar resource is a critical point to perform an economic feasibility analysis of Concentrating Solar Power (CSP) plants. This knowledge must include its magnitude (how much solar energy is available at an area of interest over a long time period), and its variability over time. In particular, DNI inter-annual variations may be large, increasing the return of investment risk in CSP plant projects. This risk is typically evaluated by means of the simulation of the energy delivered by the CSP plant during years with low solar irradiation, which are typically characterized by annual solar radiation datasets with high probability of exceedance of their annual DNI values. In this context, this paper proposes the use meteorological years representative of a given probability of exceedance of annual DNI in order to realistically assess the inter-annual variability of energy yields. The performance of this approach is evaluated in the location of Burns station (University of Oregon Solar Radiation Monitoring Laboratory), where a 34- year (from 1980 to 2013) measured data set of solar irradiance and temperature is available. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
45. Validation of an all‐sky imager–based nowcasting system for industrial PV plants.
- Author
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Kuhn, Pascal, Nouri, Bijan, Wilbert, Stefan, Prahl, Christoph, Kozonek, Nora, Schmidt, Thomas, Yasser, Zeyad, Ramirez, Lourdes, Zarzalejo, Luis, Meyer, Angela, Vuilleumier, Laurent, Heinemann, Detlev, Blanc, Philippe, and Pitz‐Paal, Robert
- Subjects
PHOTOVOLTAIC power generation ,ELECTRICITY ,NOWCASTING (Meteorology) ,ELECTRIC power distribution grids ,PYRHELIOMETER - Abstract
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 m
2 ) 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]- Published
- 2018
- Full Text
- View/download PDF
46. Applications of a shadow camera system for energy meteorology.
- Author
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Kuhn, Pascal, Wilbert, Stefan, Prahl, Christoph, Garsche, Dominik, Schüler, David, Haase, Thomas, Ramirez, Lourdes, Zarzalejo, Luis, Meyer, Angela, Blanc, Philippe, and Pitz-Paal, Robert
- Subjects
CAMERAS ,METEOROLOGY ,SOLAR power plants ,ELECTRIC power distribution grids ,RENEWABLE energy sources - Abstract
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]
- Published
- 2018
- Full Text
- View/download PDF
47. A Methodology for Calculating Percentile Values of Annual Direct Normal Solar Irradiation Series.
- Author
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Fernández Peruchena, Carlos M., Ramírez, Lourdes, Silva, Manuel, Lara, Vicente, Bermejo, Diego, Gastón, Martín, Moreno, Sara, Pulgar, Jesús, Liria, Juan, Macías, Sergio, Gonzalez, Rocio, Bernardos, Ana, Castillo, Nuria, Bolinaga, Beatriz, Valenzuela, Rita X., and Zarzalejo, Luis
- Subjects
PERCENTILES ,SOLAR radiation ,ENERGY economics ,SOLAR power plants ,CUMULATIVE distribution function - Abstract
A detailed knowledge of the solar resource is a critical point in the performance of an economic feasibility analysis of solar thermal electricity plants. In particular, the Direct Normal solar Irradiance (DNI) is the most determining variable in its final energy yield. Inter-annual variations of DNI can be large and seriously compromise the viability of solar energy projects. In this work, a methodology for evaluating the statistical properties of annual DNI series is presented for generating inputs to risk assessments in an economic feasibility analysis of a solar power plant. The methodology relies on the construction of a cumulative distribution function of annual DNI values, which allows for the evaluation of both mean and extreme climate characterization at a particular location in the long term. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
48. Solar Radiation Derived from Satellite Images.
- Author
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Badescu, Viorel, Polo, Jesús, Zarzalejo, Luis F., and Ramírez, Lourdes
- Published
- 2008
- Full Text
- View/download PDF
49. Combination of Models to Generate the First PAR Maps for Spain.
- Author
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Ferrera-Cobos, Francisco, Vindel, Jose M., Wane, Ousmane, Navarro, Ana A., Zarzalejo, Luis F., and Valenzuela, Rita X.
- Subjects
MARINE west coast climate ,MEDITERRANEAN climate ,ATMOSPHERIC models - Abstract
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]- Published
- 2021
- Full Text
- View/download PDF
50. Review of Data and Data Sources for the Assessment of the Potential of Utility-Scale Hybrid Wind–Solar PV Power Plants Deployment, under a Microgrid Scope.
- Author
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Arribas, Luis, Lechón, Yolanda, Perula, Alberto, Domínguez, Javier, Ferres, Manuel, Navarro, Jorge, Zarzalejo, Luis F., García Barquero, Carolina, and Cruz, Ignacio
- Subjects
POWER plants ,RENEWABLE energy sources ,MICROGRIDS ,COAL-fired power plants ,HYBRID power ,INDUSTRIAL costs - Abstract
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]
- Published
- 2021
- Full Text
- View/download PDF
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