{"id":"https://openalex.org/W1988510559","doi":"https://doi.org/10.1109/lgrs.2014.2314315","title":"Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes","display_name":"Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes","publication_year":2014,"publication_date":"2014-04-22","ids":{"openalex":"https://openalex.org/W1988510559","doi":"https://doi.org/10.1109/lgrs.2014.2314315","mag":"1988510559"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2014.2314315","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2314315","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060780612","display_name":"Sancho Salcedo\u2010Sanz","orcid":"https://orcid.org/0000-0002-4048-1676"},"institutions":[{"id":"https://openalex.org/I189268942","display_name":"Universidad de Alcal\u00e1","ror":"https://ror.org/04pmn0e78","country_code":"ES","type":"education","lineage":["https://openalex.org/I189268942"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Sancho Salcedo-Sanz","raw_affiliation_strings":["Department of Signal Processing and Communications, Universidad de Alcal\u00e1, Madrid, Spain","Dept. of Signal Process. & Commun., Univ. de Alcala, Alcala de Henares, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Signal Processing and Communications, Universidad de Alcal\u00e1, Madrid, Spain","institution_ids":["https://openalex.org/I189268942"]},{"raw_affiliation_string":"Dept. of Signal Process. & Commun., Univ. de Alcala, Alcala de Henares, Spain","institution_ids":["https://openalex.org/I189268942"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034384417","display_name":"C. Casanova\u2010Mateo","orcid":"https://orcid.org/0000-0002-6342-106X"},"institutions":[{"id":"https://openalex.org/I108103353","display_name":"Universidad de Valladolid","ror":"https://ror.org/01fvbaw18","country_code":"ES","type":"education","lineage":["https://openalex.org/I108103353"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Carlos Casanova-Mateo","raw_affiliation_strings":["Department of Applied Physics, Universidad de Valladolid, Valladolid, Spain","Dept. of Appl. Phys., Univ. de Valladolid, Valladolid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Physics, Universidad de Valladolid, Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]},{"raw_affiliation_string":"Dept. of Appl. Phys., Univ. de Valladolid, Valladolid, Spain","institution_ids":["https://openalex.org/I108103353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085480844","display_name":"Jordi Mu\u00f1oz-Mar\u0131\u0301","orcid":"https://orcid.org/0000-0002-3014-3921"},"institutions":[{"id":"https://openalex.org/I16097986","display_name":"Universitat de Val\u00e8ncia","ror":"https://ror.org/043nxc105","country_code":"ES","type":"education","lineage":["https://openalex.org/I16097986"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jordi Munoz-Mari","raw_affiliation_strings":["Image Processing Laboratory, Universitat de Val\u00e8ncia, Valencia, Spain","Image Process. Lab., Univ. de Valencia, Valencia, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image Processing Laboratory, Universitat de Val\u00e8ncia, Valencia, Spain","institution_ids":["https://openalex.org/I16097986"]},{"raw_affiliation_string":"Image Process. Lab., Univ. de Valencia, Valencia, Spain","institution_ids":["https://openalex.org/I16097986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039052506","display_name":"Gustau Camps\u2010Valls","orcid":"https://orcid.org/0000-0003-1683-2138"},"institutions":[{"id":"https://openalex.org/I16097986","display_name":"Universitat de Val\u00e8ncia","ror":"https://ror.org/043nxc105","country_code":"ES","type":"education","lineage":["https://openalex.org/I16097986"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Gustau Camps-Valls","raw_affiliation_strings":["Image Processing Laboratory, Universitat de Val\u00e8ncia, Valencia, Spain","Image Process. Lab., Univ. de Valencia, Valencia, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Image Processing Laboratory, Universitat de Val\u00e8ncia, Valencia, Spain","institution_ids":["https://openalex.org/I16097986"]},{"raw_affiliation_string":"Image Process. Lab., Univ. de Valencia, Valencia, Spain","institution_ids":["https://openalex.org/I16097986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.3545,"has_fulltext":false,"cited_by_count":94,"citation_normalized_percentile":{"value":0.98214794,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"11","issue":"11","first_page":"1936","last_page":"1940"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9771999716758728,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.686294674873352},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6111061573028564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6059518456459045},{"id":"https://openalex.org/keywords/radiosonde","display_name":"Radiosonde","score":0.5831720232963562},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5069906711578369},{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.47931304574012756},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4360729455947876},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4304119944572449},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4242202639579773},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3853764832019806},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36179932951927185},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.35330161452293396},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3362003564834595},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33457672595977783},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21127519011497498},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18340030312538147},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.08388710021972656}],"concepts":[{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.686294674873352},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6111061573028564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6059518456459045},{"id":"https://openalex.org/C11999413","wikidata":"https://www.wikidata.org/wiki/Q852817","display_name":"Radiosonde","level":2,"score":0.5831720232963562},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5069906711578369},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.47931304574012756},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4360729455947876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4304119944572449},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4242202639579773},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3853764832019806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36179932951927185},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.35330161452293396},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3362003564834595},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33457672595977783},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21127519011497498},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18340030312538147},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.08388710021972656},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2014.2314315","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2314315","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W264739116","https://openalex.org/W1598241312","https://openalex.org/W1746819321","https://openalex.org/W1976049118","https://openalex.org/W1981587612","https://openalex.org/W1987607942","https://openalex.org/W2002772915","https://openalex.org/W2007866903","https://openalex.org/W2008697060","https://openalex.org/W2050497240","https://openalex.org/W2069673223","https://openalex.org/W2074088705","https://openalex.org/W2082305898","https://openalex.org/W2089433206","https://openalex.org/W2099365903","https://openalex.org/W2122889842","https://openalex.org/W2130670721","https://openalex.org/W2135440643","https://openalex.org/W2142275564","https://openalex.org/W2145539952","https://openalex.org/W2164330327","https://openalex.org/W2167881994","https://openalex.org/W2501965429","https://openalex.org/W3022609586","https://openalex.org/W4211049957","https://openalex.org/W6680315742"],"related_works":["https://openalex.org/W1993966230","https://openalex.org/W4300066510","https://openalex.org/W4311388919","https://openalex.org/W2056958800","https://openalex.org/W4306887980","https://openalex.org/W4312319778","https://openalex.org/W3195168932","https://openalex.org/W1996541855","https://openalex.org/W2081545345","https://openalex.org/W4312391477"],"abstract_inverted_index":{"Solar":[0],"irradiation":[1],"prediction":[2,115],"is":[3,43,143],"an":[4,44],"important":[5],"problem":[6],"in":[7,12,52,135],"geosciences":[8],"with":[9],"direct":[10],"applications":[11],"renewable":[13],"energy.":[14],"Recently,":[15],"a":[16,79,94,101],"high":[17],"number":[18,148],"of":[19,66,74,137,149],"machine":[20],"learning":[21],"techniques":[22],"have":[23],"been":[24],"introduced":[25],"to":[26,84,146],"tackle":[27],"this":[28,58],"problem,":[29],"mostly":[30],"based":[31],"on":[32],"neural":[33],"networks":[34],"and":[35,108,139,141],"support":[36],"vector":[37],"machines.":[38],"Gaussian":[39],"process":[40],"regression":[41,128],"(GPR)":[42],"alternative":[45],"nonparametric":[46],"method":[47],"that":[48,104,119],"provided":[49],"excellent":[50],"results":[51],"other":[53],"biogeophysical":[54],"parameter":[55],"estimation.":[56],"In":[57],"letter,":[59],"we":[60,77],"evaluate":[61],"GPR":[62,123],"for":[63,86],"the":[64,70,75,87,120,147],"estimation":[65],"solar":[67],"irradiation.":[68],"Noting":[69],"nonstationary":[71],"temporal":[72,122],"behavior":[73],"signal,":[76],"develop":[78],"particular":[80],"time-based":[81],"composite":[82],"covariance":[83],"account":[85],"relevant":[88],"seasonal":[89],"signal":[90],"variations.":[91],"We":[92,117],"use":[93],"unique":[95],"meteorological":[96],"data":[97],"set":[98],"acquired":[99],"at":[100],"radiometric":[102],"station":[103],"includes":[105],"both":[106],"measurements":[107],"radiosondes,":[109],"as":[110,112],"well":[111],"numerical":[113],"weather":[114],"models.":[116],"show":[118],"so-called":[121],"outperforms":[124],"ten":[125],"state-of-the-art":[126],"statistical":[127],"algorithms":[129],"(even":[130],"when":[131],"including":[132],"time":[133],"information)":[134],"terms":[136],"accuracy":[138],"bias,":[140],"it":[142],"more":[144],"robust":[145],"predictions":[150],"used.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
