{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T18:40:15Z","timestamp":1772131215830,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Facing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to assess its potential. Remote sensing represents one of the low-cost solutions for solar energy assessment. Nevertheless, cloud cover is a main problem when validating the data. This study identifies satellite GHI profiles that cannot be used in energy production simulation. The validation is performed using parametric and non-parametric statistical tests. From the profile identified as invalid for simulation purposes, a site-adaptation methodology is proposed based on statistical learning using the machine learning algorithms \u201cBest subset selection\u201d and \u201cForward Stepwise Selection\u201d. Linear and non-linear heuristic models are also proposed. The final AS7 model is selected through RMSE, MBE and adjusted R2 indicators and is valid for any sky condition. The results show an increase in R2 from 0.607 to 0.876.<\/jats:p>","DOI":"10.3390\/rs14215496","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T06:49:02Z","timestamp":1667371742000},"page":"5496","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations"],"prefix":"10.3390","volume":"14","author":[{"given":"Mart\u00edn","family":"Mu\u00f1oz-Salcedo","sequence":"first","affiliation":[{"name":"Facultad de Ciencias e Ingenier\u00eda, Universidad Estatal de Milagro, Milagro 091051, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9021-6686","authenticated-orcid":false,"given":"Fernando","family":"Peci-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica-F\u00edsica y Termodin\u00e1mica Aplicada, Universidad de C\u00f3rdoba, 14014 C\u00f3rdoba, Spain"},{"name":"International Researcher, Universidad Ecotec, Guayaquil 092302, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7887-3934","authenticated-orcid":false,"given":"Francisco","family":"T\u00e1boas","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica-F\u00edsica y Termodin\u00e1mica Aplicada, Universidad de C\u00f3rdoba, 14014 C\u00f3rdoba, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","unstructured":"Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M.I. 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