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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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Demand-Response in Smart Buildings
| Demand-Response in Smart Buildings |
| Autore | Kolokotsa Denia |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (112 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
demand response
artificial neural network power predictions energy management genetic algorithm optimisation microgrid smart grid requests time cloud computing response time processing time resource allocation fog computing energy resource energy security energy sources Slovakia energy flexibility retrofitting interventions residential consumption electrification in the built environment smart cities smart energy management India energy efficiency low-carbon mobility water-energy nexus |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557433203321 |
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Kolokotsa Denia
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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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