Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42405
DC FieldValueLanguage
dc.contributor.authorMazorra-Aguiar, Luisen_US
dc.contributor.authorDíaz, Felipeen_US
dc.date.accessioned2018-11-09T11:39:56Z-
dc.date.available2018-11-09T11:39:56Z-
dc.date.issued2018en_US
dc.identifier.isbn978-3-319-76875-5-
dc.identifier.issn1865-3529
dc.identifier.urihttp://hdl.handle.net/10553/42405-
dc.description.abstractRenewable energy electrical generation has experienced significant growth in the recent years. Renewable energies generate electrical energy using different natural resources, such as solar radiation and wind fields. These resources present an unstable behavior because they depend on different meteorological conditions. In order to maintain the balance between input and output electrical energy into the power system, grid operators need to control and predict these fluctuating events. Indeed, forecasting methods are completely necessary to increase the proportion of renewable energies into the system (Heinemann et al. in Forecasting of solar radiation: solar energy resource management for electricity generation from local level to global scale. Nova Science Publishers, New York, 2006 [17], Wittmann et al. in IEEE J Sel Top Appl Earth Obs Remote Sens 1: 18-27, 2008 [46]). Reducing the uncertainty of natural resources, operators could reduce maintenance costs, improve the interventions in the intra-day market and optimize management decisions with nonrenewable energies supply. Many forecasting methods are used to obtain solar radiation forecasting for different time horizons. In this chapter, we will focus on several solar radiation forecasting statistical methods for intra-day time horizons using ground and exogenous data as inputs.en_US
dc.languageengen_US
dc.relation.ispartofGreen Energy and Technology
dc.sourceGreen Energy and Technology[ISSN 1865-3529], p. 171-200en_US
dc.subject3322 Tecnología energéticaen_US
dc.subject.otherIrradiance Forecasts
dc.subject.otherNeural-Networks
dc.subject.otherSky Irradiance
dc.subject.otherTime-Series
dc.subject.otherValidation
dc.subject.otherDatabase
dc.subject.otherClassification
dc.subject.otherFramework
dc.subject.otherVariables
dc.subject.otherUs
dc.titleSolar radiation forecasting with statistical modelsen_US
dc.typeinfo:eu-repo/semantics/bookPartes
dc.typeBookes
dc.identifier.doi10.1007/978-3-319-76876-2_8
dc.identifier.scopus85045983476
dc.identifier.isi000441013400009
dc.contributor.authorscopusid56971482900
dc.contributor.authorscopusid26429057600
dc.description.lastpage200-
dc.description.firstpage171-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Capítulo de libroen_US
dc.contributor.daisngid28998556
dc.contributor.daisngid3919769
dc.contributor.wosstandardWOS:Mazorra-Aguiar, L
dc.contributor.wosstandardWOS:Diaz, F
dc.date.coverdateEnero 2018
dc.identifier.ulpgces
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptDepartamento de Ingeniería Eléctrica-
crisitem.author.orcid0000-0002-9746-7461-
crisitem.author.orcid0000-0001-7874-6636-
crisitem.author.fullNameMazorra Aguiar, Luis-
crisitem.author.fullNameDíaz Reyes, Felipe-
Appears in Collections:Capítulo de libro
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