LEADER 04135nam 22009013a 450 001 9910674025103321 005 20250203235429.0 010 $a9783039210992 010 $a3039210998 024 8 $a10.3390/books978-3-03921-099-2 035 $a(CKB)4100000010106113 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/43703 035 $a(ScCtBLL)a44c689a-0173-4ea8-85cb-b38c28b3ed70 035 $a(OCoLC)1163834549 035 $a(EXLCZ)994100000010106113 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aComputational Intelligence in Photovoltaic Systems$fEmanuele Ogliari, Sonia Leva 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (180 p.) 311 08$a9783039210985 311 08$a303921098X 330 $aPhotovoltaics, 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. 610 $aartificial neural network 610 $aonline diagnosis 610 $agenetic algorithm 610 $arenewable energy 610 $aunit commitment 610 $aphotovoltaic panel 610 $apower forecasting 610 $ametaheuristic 610 $amonitoring system 610 $aembedded systems 610 $afirefly algorithm 610 $atracking system 610 $aMPPT algorithm 610 $aintegrated storage 610 $aday-ahead forecast 610 $asolar radiation 610 $aprototype model 610 $aartificial neural networks 610 $aparameter extraction 610 $athermal image 610 $athermal model 610 $asolar cell 610 $aPV cell temperature 610 $aevolutionary algorithms 610 $auncertainty 610 $abattery 610 $aharmony search meta-heuristic algorithm 610 $asingle-diode photovoltaic model 610 $asymbiotic organisms search 610 $aphotovoltaics 610 $atilt angle 610 $asmart photovoltaic system blind 610 $aorientation 610 $aphotovoltaic 610 $aparticle swarm optimization 610 $aanalytical methods 610 $acomputational intelligence 610 $astatistical errors 610 $aensemble methods 610 $asolar photovoltaic 610 $aelectrical parameters 610 $ademand response 610 $ametaheuristic algorithm 700 $aOgliari$b Emanuele$01338088 702 $aLeva$b Sonia 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910674025103321 996 $aComputational Intelligence in Photovoltaic Systems$93057826 997 $aUNINA