{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T14:28:49Z","timestamp":1775572129737,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Junta de Andaluc\u00eda","award":["1260136"],"award-info":[{"award-number":["1260136"]}]},{"name":"Junta de Andaluc\u00eda","award":["PID2019-107455RB-C22"],"award-info":[{"award-number":["PID2019-107455RB-C22"]}]},{"name":"Junta de Andaluc\u00eda","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]},{"name":"I+D+i project","award":["1260136"],"award-info":[{"award-number":["1260136"]}]},{"name":"I+D+i project","award":["PID2019-107455RB-C22"],"award-info":[{"award-number":["PID2019-107455RB-C22"]}]},{"name":"I+D+i project","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]},{"name":"Comunidad de Madrid Excellence Program","award":["1260136"],"award-info":[{"award-number":["1260136"]}]},{"name":"Comunidad de Madrid Excellence Program","award":["PID2019-107455RB-C22"],"award-info":[{"award-number":["PID2019-107455RB-C22"]}]},{"name":"Comunidad de Madrid Excellence Program","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.<\/jats:p>","DOI":"10.3390\/rs15092328","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T04:36:15Z","timestamp":1682656575000},"page":"2328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques"],"prefix":"10.3390","volume":"15","author":[{"given":"Miguel","family":"L\u00f3pez-Cuesta","sequence":"first","affiliation":[{"name":"Andalusian Institute for Earth System Research IISTA-CEAMA, Department of Physics, University of Jaen, 23071 Jaen, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7472-4840","authenticated-orcid":false,"given":"Ricardo","family":"Aler-Mur","sequence":"additional","affiliation":[{"name":"EVANNAI Research Group, Department of Computing Science, University Carlos III, 28911 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8490-7296","authenticated-orcid":false,"given":"In\u00e9s Mar\u00eda","family":"Galv\u00e1n-Le\u00f3n","sequence":"additional","affiliation":[{"name":"EVANNAI Research Group, Department of Computing Science, University Carlos III, 28911 Madrid, Spain"}]},{"given":"Francisco Javier","family":"Rodr\u00edguez-Ben\u00edtez","sequence":"additional","affiliation":[{"name":"Andalusian Institute for Earth System Research IISTA-CEAMA, Department of Physics, University of Jaen, 23071 Jaen, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1135-4926","authenticated-orcid":false,"given":"Antonio David","family":"Pozo-V\u00e1zquez","sequence":"additional","affiliation":[{"name":"Andalusian Institute for Earth System Research IISTA-CEAMA, Department of Physics, University of Jaen, 23071 Jaen, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"ref_1","unstructured":"Murdock, H.E., Gibb, D., Andre, T., Sawin, J.L., Brown, A., Ranalder, L., Collier, U., Dent, C., Epp, B., and Hareesh Kumar, C. (2023, February 02). Renewables 2021-Global Status Report. Available online: https:\/\/www.ren21.net\/wp-content\/uploads\/2019\/05\/GSR2021_Full_Report.pdf."},{"key":"ref_2","unstructured":"Renn\u00e9, D.S. (2014). Weather Matters for Energy, Springer."},{"key":"ref_3","unstructured":"Haupt, S.E. (2018). Weather & Climate Services for the Energy Industry, Palgrave Macmillan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112348","DOI":"10.1016\/j.rser.2022.112348","article-title":"A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality","volume":"161","author":"Yang","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rser.2013.06.042","article-title":"Review of solar irradiance forecasting methods and a proposition for small-scale insular grids","volume":"27","author":"Diagne","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.pecs.2013.06.002","article-title":"Solar forecasting methods for renewable energy integration","volume":"39","author":"Inman","year":"2013","journal-title":"Prog. Energy Combust. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.solener.2017.11.023","article-title":"History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining","volume":"168","author":"Yang","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111187","DOI":"10.1016\/j.rser.2021.111187","article-title":"Applications for solar irradiance nowcasting in the control of microgrids: A review","volume":"147","author":"Samu","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apenergy.2018.03.010","article-title":"Peer-to-Peer energy trading in a Microgrid","volume":"220","author":"Zhang","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1109\/TSTE.2017.2747765","article-title":"Short-term spatio-temporal forecasting of photovoltaic power production","volume":"9","author":"Agoua","year":"2017","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.apenergy.2017.09.063","article-title":"Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting","volume":"208","author":"Heng","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_12","unstructured":"Kleissl, J. (2013). Solar Energy Forecasting and Resource Assessment, Academic Press."},{"key":"ref_13","unstructured":"Kariniotakis, G. (2017). Renewable Energy Forecasting, Woodhead Publishing."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116838","DOI":"10.1016\/j.apenergy.2021.116838","article-title":"Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery","volume":"292","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.solener.2015.05.037","article-title":"3D cloud detection and tracking system for solar forecast using multiple sky imagers","volume":"118","author":"Peng","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3399","DOI":"10.5194\/acp-16-3399-2016","article-title":"Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts","volume":"16","author":"Schmidt","year":"2016","journal-title":"Atmos. Chem. Phys"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nouri, B., Wilbert, S., Kuhn, P., Hanrieder, N., Schroedter-Homscheidt, M., Kazantzidis, A., Zarzalejo, L., Blanc, P., Kumar, S., and Goswami, N. (2019). Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts. Remote Sens., 11.","DOI":"10.3390\/rs11091059"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dittmann, A., Holland, N., and Lorenz, E. (2021). A new sky imager based global irradiance forecasting model with analyses of cirrus situations. Meteorol. Z., 101\u2013113.","DOI":"10.1127\/metz\/2020\/1024"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"080001","DOI":"10.1063\/1.4949181","article-title":"Solar thermal energy predictability for the grid (STEP4Grid)","volume":"Volume 1734","author":"Pacheco","year":"2016","journal-title":"Proceedings of the AIP Conference Proceedings"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.solener.2020.01.045","article-title":"Optimization of parabolic trough power plant operations in variable irradiance conditions using all sky imagers","volume":"198","author":"Nouri","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1051\/rees\/2017028","article-title":"Short-term solar forecasting based on sky images to enable higher PV generation in remote electricity networks","volume":"2","author":"Schmidt","year":"2017","journal-title":"Renew. Energy Environ. Sustain."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.renene.2018.06.058","article-title":"A method for detailed, short-term energy yield forecasting of photovoltaic installations","volume":"130","author":"Anagnostos","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_23","unstructured":"Sayigh, A. (2012). Comprehensive Renewable Energy, Elsevier."},{"key":"ref_24","unstructured":"Blanc, P., Remund, J., and Vallance, L. (2017). Renewable Energy Forecasting, Elsevier."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Blanco, M.J., and Santigosa, L.R. (2017). Advances in Concentrating Solar Thermal Research and Technology, Woodhead Publishing.","DOI":"10.1016\/B978-0-08-100516-3.00001-0"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.solener.2019.11.028","article-title":"A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment","volume":"195","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/BAMS-D-19-0304.1","article-title":"Meteosat Third Generation (MTG): Continuation and innovation of observations from geostationary orbit","volume":"102","author":"Holmlund","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Prasad, A.A., and Kay, M. (2021). Prediction of Solar Power Using Near-Real Time Satellite Data. Energies, 14.","DOI":"10.3390\/en14185865"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.solener.2018.01.080","article-title":"Optimal solar tracking strategy to increase irradiance in the plane of array under cloudy conditions: A study across Europe","volume":"163","author":"Antonanzas","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1127\/metz\/2019\/0925","article-title":"Impact of DNI nowcasting on annual revenues of CSP plants for a time of delivery based feed in tariff","volume":"28","author":"Dersch","year":"2019","journal-title":"Meteorol. Z."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.renene.2019.01.095","article-title":"Impact of DNI forecasting on CSP tower plant power production","volume":"138","author":"Polo","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.apenergy.2018.03.154","article-title":"Assessment of forecasting methods on performance of photovoltaic-battery systems","volume":"221","author":"Litjens","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MPE.2019.2932639","article-title":"The use of probabilistic forecasts: Applying them in theory and practice","volume":"17","author":"Haupt","year":"2019","journal-title":"IEEE Power Energy Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.renene.2016.12.095","article-title":"Machine learning methods for solar radiation forecasting: A review","volume":"105","author":"Voyant","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109792","DOI":"10.1016\/j.rser.2020.109792","article-title":"A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization","volume":"124","author":"Ahmed","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.rser.2019.02.006","article-title":"Automatic hourly solar forecasting using machine learning models","volume":"105","author":"Yagli","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1175\/1520-0477(1995)076<1157:IMOSFT>2.0.CO;2","article-title":"Improved model output and statistics through model consensus","volume":"76","author":"Vislocky","year":"1995","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_38","unstructured":"Lorenz, E., K\u00fchnert, J., and Heinemann, D. (2012, January 24\u201328). Short term forecasting of solar irradiance by combining satellite data and numerical weather predictions. Proceedings of the 27th European PV Solar Energy Conference (EU PVSEC), Frankfurt, Germany."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1175\/BAMS-D-16-0221.1","article-title":"Building the Sun4Cast System: Improvements in Solar Power Forecasting","volume":"99","author":"Haupt","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2100442","DOI":"10.1002\/solr.202100442","article-title":"A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy","volume":"6","author":"Nouri","year":"2022","journal-title":"Solar RRL"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"E1016","DOI":"10.1175\/BAMS-D-20-0031.1","article-title":"Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences","volume":"102","author":"Boukabara","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113960","DOI":"10.1016\/j.enconman.2021.113960","article-title":"A review on global solar radiation prediction with machine learning models in a comprehensive perspective","volume":"235","author":"Zhou","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.renene.2018.02.006","article-title":"Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts","volume":"123","author":"Pedro","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.solener.2019.06.041","article-title":"Short-term solar power forecast with deep learning: Exploring optimal input and output configuration","volume":"188","author":"Sun","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.energy.2018.09.116","article-title":"Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability","volume":"165","author":"Fouilloy","year":"2018","journal-title":"Energy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"117743","DOI":"10.1016\/j.energy.2020.117743","article-title":"Combining forecasts of day-ahead solar power","volume":"202","author":"Dewangan","year":"2020","journal-title":"Energy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.solener.2016.05.051","article-title":"Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data","volume":"135","author":"Wolff","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.renene.2016.06.018","article-title":"Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting","volume":"97","author":"Aguiar","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","article-title":"Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM","volume":"148","author":"Qing","year":"2018","journal-title":"Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.solener.2019.11.091","article-title":"A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning","volume":"195","author":"Aler","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_51","unstructured":"Long, C.N., and Dutton, E.G. (2023, February 02). BSRN Global Network Recommended QC Tests, V2. x. Available online: https:\/\/epic.awi.de\/id\/eprint\/30083\/1\/BSRN_recommended_QC_tests_V2.pdf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1175\/JTECH-D-11-00009.1","article-title":"A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images","volume":"28","author":"Li","year":"2011","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Revaud, J., Harchaoui, Z., and Schmid, C. (2013, January 1\u20138). DeepFlow: Large displacement optical flow with deep matching. Proceedings of the ICCV-IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.175"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0038-092X(99)00055-9","article-title":"On the clear sky model of the ESRA\u2014European Solar Radiation Atlas\u2014with respect to the Heliosat method","volume":"68","author":"Rigollier","year":"2000","journal-title":"Sol. Energy"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1175\/BAMS-83-7-Schmetz-2","article-title":"An introduction to Meteosat second generation (MSG)","volume":"83","author":"Schmetz","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.solener.2004.04.017","article-title":"The method Heliosat-2 for deriving shortwave solar radiation from satellite images","volume":"77","author":"Rigollier","year":"2004","journal-title":"Sol. Energy"},{"key":"ref_57","unstructured":"Liberzon, A., Gurka, R., and Taylor, Z. (2023, February 02). Openpiv Home Page. Available online: https:\/\/openpiv.sourceforge.net\/."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.solener.2014.07.026","article-title":"Streamline-based method for intra-day solar forecasting through remote sensing","volume":"108","author":"Nonnenmacher","year":"2014","journal-title":"Sol. Energy"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Ma, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref_60","first-page":"431","article-title":"Understanding variable importances in forests of randomized trees","volume":"26","author":"Louppe","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_61","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.3390\/rs70709070","article-title":"Short-term forecasting of surface solar irradiance based on Meteosat-SEVIRI data using a nighttime cloud index","volume":"7","author":"Hammer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Romano, F., Cimini, D., Cersosimo, A., Di Paola, F., Gallucci, D., Gentile, S., Geraldi, E., Larosa, S., Nilo, S.T., and Ricciardelli, E. (2018). Improvement in surface solar irradiance estimation using HRV\/MSG data. Remote Sens., 10.","DOI":"10.3390\/rs10081288"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.3390\/rs5052475","article-title":"Geometric accuracy investigations of SEVIRI high resolution visible (HRV) level 1.5 Imagery","volume":"5","author":"Aksakal","year":"2013","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"118645","DOI":"10.1016\/j.atmosenv.2021.118645","article-title":"On the geometric accuracy and stability of MSG SEVIRI images","volume":"262","author":"Debaecker","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.solener.2017.11.049","article-title":"Short-term solar irradiance forecasting via satellite\/model coupling","volume":"168","author":"Miller","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"409","DOI":"10.5194\/amt-10-409-2017","article-title":"Cloud and DNI nowcasting with MSG\/SEVIRI for the optimized operation of concentrating solar power plants","volume":"10","author":"Sirch","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Cuesta, M., Jim\u00e9nez-Garrote, A., Aler-Mur, R., Galv\u00e1n-Le\u00f3n, I., Tovar-Pescador, J., and Pozo-Vazquez, D. (2022, January 4\u20139). Improving ASI-Based Solar Radiation Nowcasting by Using Automatic Cloud Type Recognition Methods (No. EMS2022-166). Proceedings of the Copernicus Meetings, Bonn, Germany. Technical Report.","DOI":"10.5194\/ems2022-166"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_70","first-page":"983","article-title":"Quantile regression forests","volume":"7","author":"Meinshausen","year":"2006","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2328\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:25:30Z","timestamp":1760124330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":70,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092328"],"URL":"https:\/\/doi.org\/10.3390\/rs15092328","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]}}}