Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications
This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and system...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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072 | 7 | |a TBX |2 bicssc | |
100 | 1 | |a Vitelli, Massimo |4 edt | |
700 | 1 | |a Costanzo, Luigi |4 edt | |
700 | 1 | |a Vitelli, Massimo |4 oth | |
700 | 1 | |a Costanzo, Luigi |4 oth | |
245 | 1 | 0 | |a Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications |
260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
300 | |a 1 electronic resource (280 p.) | ||
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338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a sensor network | ||
653 | |a data fusion | ||
653 | |a complex network analysis | ||
653 | |a fault prognosis | ||
653 | |a photovoltaic plants | ||
653 | |a ANFIS | ||
653 | |a statistical method | ||
653 | |a gradient descent | ||
653 | |a photovoltaic system | ||
653 | |a sustainable development | ||
653 | |a PV power prediction | ||
653 | |a artificial neural network | ||
653 | |a renewable energy | ||
653 | |a environmental parameters | ||
653 | |a multiple regression model | ||
653 | |a moth-flame optimization | ||
653 | |a parameter extraction | ||
653 | |a photovoltaic model | ||
653 | |a double flames generation (DFG) strategy | ||
653 | |a Solar cell parameters | ||
653 | |a single-diode model | ||
653 | |a two-diode model | ||
653 | |a COA | ||
653 | |a photovoltaic systems | ||
653 | |a maximum power point tracking | ||
653 | |a single stage grid connected systems | ||
653 | |a solar concentrator | ||
653 | |a spectral beam splitting | ||
653 | |a diffractive optical element | ||
653 | |a diffractive grating | ||
653 | |a PVs power output forecasting | ||
653 | |a adaptive neuro-fuzzy inference systems | ||
653 | |a particle swarm optimization-artificial neural networks | ||
653 | |a solar irradiation | ||
653 | |a photovoltaic power prediction | ||
653 | |a publicly available weather reports | ||
653 | |a machine learning | ||
653 | |a long short-term memory | ||
653 | |a integrated energy systems | ||
653 | |a smart energy management | ||
653 | |a PV fleet | ||
653 | |a clustering-based PV fault detection | ||
653 | |a unsupervised learning | ||
653 | |a self-imputation | ||
653 | |a implicit model solution | ||
653 | |a photovoltaic array | ||
653 | |a series-parallel | ||
653 | |a global optimization | ||
653 | |a partial shading | ||
653 | |a deterministic optimization algorithm | ||
653 | |a metaheuristic optimization algorithm | ||
653 | |a genetic algorithm | ||
653 | |a solar cell optimization | ||
653 | |a finite difference time domain | ||
653 | |a optical modelling | ||
653 | |a thermal image | ||
653 | |a photovoltaic module | ||
653 | |a hot spot | ||
653 | |a image processing | ||
653 | |a deterioration | ||
653 | |a linear approximation | ||
653 | |a MPPT algorithm | ||
653 | |a duty cycle | ||
653 | |a global horizontal irradiance | ||
653 | |a mathematical modeling | ||
653 | |a feed-forward neural networks | ||
653 | |a recurrent neural networks | ||
653 | |a LSTM cell | ||
653 | |a performances evaluation | ||
653 | |a clear sky irradiance | ||
653 | |a persistent predictor | ||
653 | |a photovoltaics | ||
653 | |a artificial neural networks | ||
653 | |a national power system | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2934 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/69162 |7 0 |z DOAB: description of the publication |