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Machine Learning for Energy Systems
| Machine Learning for Energy Systems |
| Autore | Sidorov Denis N |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (272 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
abnormal defects
Adaptive Neuro-Fuzzy Inference System artificial intelligence blockchain blockchain technology cast-resin transformers classification classification and regression trees clustering component accident set cyber-physical systems data evolution mechanism decision tree energy internet energy management system energy router energy storage energy systems ensemble empirical mode decomposition extreme learning machine fatigue forecasting harmonic impedance harmonic impedance identification harmonic parameter harmonic responsibility hierarchical clustering high permeability renewable energy hybrid AC/DC power system hybrid interval forecasting industrial mathematics information security insulator fault forecast integrated energy system intelligent control Interfacial tension inverse problems linear regression model linearization load leveling machine learning maintenance monitoring data without phase angle MOPSO algorithm offshore wind farm optimization parameter estimation partial discharge pattern recognition photovoltaic output power forecasting power control power quality QoS index of energy flow relevance vector machine renewable energy source risk assessment rule extraction sample entropy scheduling optimization smart microgrid stochastic optimization time series forecasting traction network transformer oil parameters vacuum tank degasser Volterra equations Volterra models vulnerability wavelet packets wind power: wind speed: T-S fuzzy model: forecasting wind turbine |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557678803321 |
Sidorov Denis N
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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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Overcoming Data Scarcity in Earth Science
| Overcoming Data Scarcity in Earth Science |
| Autore | Etcheverry Venturini Lorena |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (94 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
3D-Var
arthropod vector attribute reduction climate extreme indices (CEIs) ClimPACT core attribute data assimilation data imputation data quality data scarcity Dataset Licensedatabase decision trees earth-science data ensemble learning environmental modeling environmental observations Expert Team on Climate Change Detection and Indices (ETCCDI) Expert Team on Sector-specific Climate Indices (ET-SCI) geophysical monitoring GLDAS invasive species k-Nearest Neighbors machine learning magnetotelluric monitoring microhabitat missing data multi-class classification processing remote sensing rough set theory rule extraction soil texture calculator species distribution modeling statistical methods support vector machines water quality |
| ISBN | 3-03928-211-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404080803321 |
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Etcheverry Venturini Lorena
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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