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|a 9783039210992
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|a books978-3-03921-099-2
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|a 9783039210985
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|a Ogliari , Emanuele
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| 245 |
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|a Computational Intelligence in Photovoltaic Systems
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2019
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| 300 |
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|a 1 electronic resource (180 p.)
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a tilt angle
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| 653 |
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|a monitoring system
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| 653 |
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|a renewable energy
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| 653 |
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|a genetic algorithm
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| 653 |
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|a parameter extraction
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| 653 |
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|a smart photovoltaic system blind
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| 653 |
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|a solar radiation
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| 653 |
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|a solar photovoltaic
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| 653 |
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|a uncertainty
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| 653 |
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|a orientation
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a photovoltaic panel
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|a power forecasting
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| 653 |
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|a embedded systems
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|a ensemble methods
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|a artificial neural network
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|a symbiotic organisms search
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|a unit commitment
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|a tracking system
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| 653 |
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|a thermal image
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| 653 |
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|a integrated storage
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| 653 |
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|a evolutionary algorithms
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|a harmony search meta-heuristic algorithm
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| 653 |
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|a analytical methods
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| 653 |
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|a demand response
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| 653 |
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|a day-ahead forecast
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| 653 |
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|a thermal model
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| 653 |
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|a photovoltaic
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|a electrical parameters
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| 653 |
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|a photovoltaics
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|a battery
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|a firefly algorithm
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|a single-diode photovoltaic model
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|a PV cell temperature
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|a computational intelligence
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|a MPPT algorithm
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|a solar cell
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|a artificial neural networks
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|a prototype model
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|a statistical errors
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|a metaheuristic
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|a online diagnosis
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|a metaheuristic algorithm
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1 |
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|a Leva, Sonia
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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|a 10.3390/books978-3-03921-099-2
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/1541
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/43703
|z DOAB: description of the publication
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|a 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.
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