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Computational Intelligence in Photovoltaic Systems

dc.contributor.authorOgliari , Emanuele*
dc.contributor.authorLeva, Sonia*
dc.date.accessioned2021-02-11T10:19:04Z
dc.date.available2021-02-11T10:19:04Z
dc.date.issued2019*
dc.date.submitted2019-12-09 16:10:12*
dc.identifier42694*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/43703
dc.description.abstractPhotovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.*
dc.languageEnglish*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.otherartificial neural network*
dc.subject.otheronline diagnosis*
dc.subject.othergenetic algorithm*
dc.subject.otherrenewable energy*
dc.subject.otherunit commitment*
dc.subject.otherphotovoltaic panel*
dc.subject.otherpower forecasting*
dc.subject.othermetaheuristic*
dc.subject.othermonitoring system*
dc.subject.otherembedded systems*
dc.subject.otherfirefly algorithm*
dc.subject.othertracking system*
dc.subject.otherMPPT algorithm*
dc.subject.otherintegrated storage*
dc.subject.otherday-ahead forecast*
dc.subject.othersolar radiation*
dc.subject.otherprototype model*
dc.subject.otherartificial neural networks*
dc.subject.otherparameter extraction*
dc.subject.otherthermal image*
dc.subject.otherthermal model*
dc.subject.othersolar cell*
dc.subject.otherPV cell temperature*
dc.subject.otherevolutionary algorithms*
dc.subject.otheruncertainty*
dc.subject.otherbattery*
dc.subject.otherharmony search meta-heuristic algorithm*
dc.subject.othersingle-diode photovoltaic model*
dc.subject.othersymbiotic organisms search*
dc.subject.otherphotovoltaics*
dc.subject.othertilt angle*
dc.subject.othersmart photovoltaic system blind*
dc.subject.otherorientation*
dc.subject.otherphotovoltaic*
dc.subject.otherparticle swarm optimization*
dc.subject.otheranalytical methods*
dc.subject.othercomputational intelligence*
dc.subject.otherstatistical errors*
dc.subject.otherensemble methods*
dc.subject.othersolar photovoltaic*
dc.subject.otherelectrical parameters*
dc.subject.otherdemand response*
dc.subject.othermetaheuristic algorithm*
dc.titleComputational Intelligence in Photovoltaic Systems*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-099-2*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039210992*
oapen.relation.isbn9783039210985*
oapen.pages180*
oapen.edition1st*


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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/