{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:27:02Z","timestamp":1760243222346,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2014,4,11]],"date-time":"2014-04-11T00:00:00Z","timestamp":1397174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of \u00b12 standard deviations).<\/jats:p>","DOI":"10.3390\/s140406758","type":"journal-article","created":{"date-parts":[[2014,4,11]],"date-time":"2014-04-11T11:36:49Z","timestamp":1397216209000},"page":"6758-6787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Spatial Estimation of Sub-Hour Global Horizontal Irradiance Based on Official Observations and Remote Sensors"],"prefix":"10.3390","volume":"14","author":[{"given":"Federico-Vladimir","family":"Gutierrez-Corea","sequence":"first","affiliation":[{"name":"ETSI Topography, Geodetics and Cartography, Universidad Polit\u00e9cnica de Madrid, Campus Sur UPM, Autov\u00eda de Valencia Km 7.5, E-28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-8639","authenticated-orcid":false,"given":"Miguel-Angel","family":"Manso-Callejo","sequence":"additional","affiliation":[{"name":"ETSI Topography, Geodetics and Cartography, Universidad Polit\u00e9cnica de Madrid, Campus Sur UPM, Autov\u00eda de Valencia Km 7.5, E-28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda-Pilar","family":"Moreno-Regidor","sequence":"additional","affiliation":[{"name":"ETSI Topography, Geodetics and Cartography, Universidad Polit\u00e9cnica de Madrid, Campus Sur UPM, Autov\u00eda de Valencia Km 7.5, E-28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas","family":"Velasco-G\u00f3mez","sequence":"additional","affiliation":[{"name":"ETSI Topography, Geodetics and Cartography, Universidad Polit\u00e9cnica de Madrid, Campus Sur UPM, Autov\u00eda de Valencia Km 7.5, E-28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11571","DOI":"10.3390\/s120911571","article-title":"A Study of the Relationship between Weather Variables and Electric Power Demand Inside a Smart grid\/smart World Framework","volume":"12","author":"Aguiar","year":"2012","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1016\/j.neucom.2008.09.010","article-title":"Accurate Short-Term Wind Speed Prediction by Exploiting Diversity in Input Data using Banks of Artificial Neural Networks","volume":"72","author":"Prieto","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1139\/x87-081","article-title":"Extrapolation of Synoptic Meteorological Data in Mountainous Terrain and its use for Simulating Forest Evapotranspiration and Photosynthesis","volume":"17","author":"Running","year":"1987","journal-title":"Can. 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