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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 online resource (280 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
adaptive neuro-fuzzy inference systems
ANFIS artificial neural network artificial neural networks clear sky irradiance clustering-based PV fault detection COA complex network analysis data fusion deterioration deterministic optimization algorithm diffractive grating diffractive optical element double flames generation (DFG) strategy duty cycle environmental parameters fault prognosis feed-forward neural networks finite difference time domain genetic algorithm global horizontal irradiance global optimization gradient descent hot spot image processing implicit model solution integrated energy systems linear approximation long short-term memory LSTM cell machine learning mathematical modeling maximum power point tracking metaheuristic optimization algorithm moth-flame optimization MPPT algorithm multiple regression model national power system optical modelling parameter extraction partial shading particle swarm optimization-artificial neural networks performances evaluation persistent predictor photovoltaic array photovoltaic model photovoltaic module photovoltaic plants photovoltaic power prediction photovoltaic system photovoltaic systems photovoltaics publicly available weather reports PV fleet PV power prediction PVs power output forecasting recurrent neural networks renewable energy self-imputation sensor network series-parallel single stage grid connected systems single-diode model smart energy management solar cell optimization Solar cell parameters solar concentrator solar irradiation spectral beam splitting statistical method sustainable development thermal image two-diode model unsupervised learning |
| 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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Probabilistic and Fuzzy Approaches for Estimating the Life Cycle Costs of Buildings
| Probabilistic and Fuzzy Approaches for Estimating the Life Cycle Costs of Buildings |
| Autore | Plebankiewicz Edyta |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (220 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
Bayes conditional probability
bidding decision buildings cause-effect relationships CBA classification commercial and recreational complex building projects complex evaluation construction COPRAS and INVAR methods critical risk factors damage defects drift ratio dynamic analysis economic evaluation European Union Member States fuzzy sets historical buildings implementation factors information and communication technology intensity investment project LCC criterion life cycle life cycle costs lifecycles maintenance marketing MCDM multiple criteria analysis occurrences operation and maintenance phase optimization price criterion probability distribution probability of winning railway infrastructure reliability residential buildings risk assessment risk identification sensitivity analyses socioeconomic impact statistical method steel frames substitution success and image of a country sustainable construction industry technical wear tenement houses Tuned Mass Damper |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557659003321 |
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Plebankiewicz Edyta
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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