Joaquín Alonso Montesinos has completed his PhD at the age of 26 years from the University of Almería, Spain and postdoctoral studies from the same University. He is member of a research group ‘Solar energetic resources, Climatology and Atmospheric physics’ at the University of Almería. He has published more than 10 papers in reputed journals, has authored and coauthored different conference papers, both national and international, and has participated in different projects related to solar radiation. Furthermore, he is serving as reviewer in different impact journals.
Predicting atmospheric features is key to managing solar plants and is therefore necessary for correct electrical grid management. An unexpected atmospheric change can provoke a range of problems related to various solar plant components affecting the electricity generation system and, in consequence, causing alterations in the electricity grid. Mainly, changes are produced by cloud transients which make that solar radiation decreases a lot in a short temporal space. For that reason, to have the knowledge about future alterations in the solar resource is very important to avoid problems in the components of solar power plants and in the electricity integration into the electrical grid. In this work, a methodology based on remote sensing techniques has been developed to predict Direct Normal Irradiance (DNI) for one hour ahead, in 15 minutes periods, where Meteosat Second Generation satellite images were processed to identify cloud motions and to predict the DNI according to these motions. Normalized root-mean square error (nRMSE) presented a value of about 30% from 15 minutes to one hour
Gabriel López studied Theoretical Physics at the Universidad de Granada (Spain). He completed his PhD from the Universidad de Almería (Spain). He is currently an Associate Professor at the Escuela Técnica Superior de Ingeniería of the Universidad de Huelva (Spain). He has co-authored more than 20 articles in reputed journals. The main research topic is the solar resource modeling and forecasting using artificial intelligence techniques and remote sensing. At present, he leads one of the three research teams of the Spanish national project PRESOL.
Determination of atmospheric aerosol properties is a key step in many meteorological and energy fields. This is the case for solar concentrating systems, where atmospheric turbidity is the main attenuating factor under cloudless sky conditions. Remote sensing and ground-based observations have experienced large advances in the retrieval of several aerosol parameters in recent years. Different methods have been developed for automatic detection of cloudless conditions. Unfortunately, these automatic cloud screening algorithms are not totally reliable, and could lead to biased results. In this study, we analyze the efficiency of the cloud screening method used in the Aerosol Robotic Network (AERONET) to provide their Level 1.5 data from raw Level 1.0 data from the radiometric site located in Huelva (Spain). The presence of clouds is detected from visual inspection of digital images obtained by an all-sky camera, which is located/co-located with the AERONET sunphotomer. A total of 240 days with concurrent data for both the sun photometer and the all-sky camera were available for the year 2015. Results show that about 10% of Level 1.5 sun photometric data are contaminated with clouds. On the other hand, we also found cloud-free sky cases where raw Level 1.0 data were removed from Level 1.5. The effect of misclassification on the average daily aerosol optical depth is also presented.