TY - BOOK ID - 61122230 TI - Computational Intelligence in Photovoltaic Systems AU - Ogliari , Emanuele AU - Leva, Sonia PY - 2019 SN - 3039210998 303921098X PB - MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - artificial neural network KW - online diagnosis KW - genetic algorithm KW - renewable energy KW - unit commitment KW - photovoltaic panel KW - power forecasting KW - metaheuristic KW - monitoring system KW - embedded systems KW - firefly algorithm KW - tracking system KW - MPPT algorithm KW - integrated storage KW - day-ahead forecast KW - solar radiation KW - prototype model KW - artificial neural networks KW - parameter extraction KW - thermal image KW - thermal model KW - solar cell KW - PV cell temperature KW - evolutionary algorithms KW - uncertainty KW - battery KW - harmony search meta-heuristic algorithm KW - single-diode photovoltaic model KW - symbiotic organisms search KW - photovoltaics KW - tilt angle KW - smart photovoltaic system blind KW - orientation KW - photovoltaic KW - particle swarm optimization KW - analytical methods KW - computational intelligence KW - statistical errors KW - ensemble methods KW - solar photovoltaic KW - electrical parameters KW - demand response KW - metaheuristic algorithm UR - https://www.unicat.be/uniCat?func=search&query=sysid:61122230 AB - Photovoltaics, 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. ER -