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Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications
| Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications |
| Autore | Vitelli Massimo |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (280 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
sensor network
data fusion complex network analysis fault prognosis photovoltaic plants ANFIS statistical method gradient descent photovoltaic system sustainable development PV power prediction artificial neural network renewable energy environmental parameters multiple regression model moth-flame optimization parameter extraction photovoltaic model double flames generation (DFG) strategy Solar cell parameters single-diode model two-diode model COA photovoltaic systems maximum power point tracking single stage grid connected systems solar concentrator spectral beam splitting diffractive optical element diffractive grating PVs power output forecasting adaptive neuro-fuzzy inference systems particle swarm optimization-artificial neural networks solar irradiation photovoltaic power prediction publicly available weather reports machine learning long short-term memory integrated energy systems smart energy management PV fleet clustering-based PV fault detection unsupervised learning self-imputation implicit model solution photovoltaic array series–parallel global optimization partial shading deterministic optimization algorithm metaheuristic optimization algorithm genetic algorithm solar cell optimization finite difference time domain optical modelling thermal image photovoltaic module hot spot image processing deterioration linear approximation MPPT algorithm duty cycle global horizontal irradiance mathematical modeling feed-forward neural networks recurrent neural networks LSTM cell performances evaluation clear sky irradiance persistent predictor photovoltaics artificial neural networks national power system |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557297703321 |
Vitelli Massimo
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational Intelligence in Photovoltaic Systems
| Computational Intelligence in Photovoltaic Systems |
| Autore | Ogliari Emanuele |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (180 p.) |
| Soggetto non controllato |
artificial neural network
online diagnosis genetic algorithm renewable energy unit commitment photovoltaic panel power forecasting metaheuristic monitoring system embedded systems firefly algorithm tracking system MPPT algorithm integrated storage day-ahead forecast solar radiation prototype model artificial neural networks parameter extraction thermal image thermal model solar cell PV cell temperature evolutionary algorithms uncertainty battery harmony search meta-heuristic algorithm single-diode photovoltaic model symbiotic organisms search photovoltaics tilt angle smart photovoltaic system blind orientation photovoltaic particle swarm optimization analytical methods computational intelligence statistical errors ensemble methods solar photovoltaic electrical parameters demand response metaheuristic algorithm |
| ISBN | 3-03921-099-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910674025103321 |
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Ogliari Emanuele
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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