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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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Indoor Environment Quality and Health in Energy-Efficient Buildings
| Indoor Environment Quality and Health in Energy-Efficient Buildings |
| Autore | González Lezcano Roberto Alonso |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (174 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
air temperature measurements
airtightness architecture basalt fiber basalt waste aggregate building evaluation climate change COVID-19 data analysis educational buildings efficient buildings energy efficiency epidemiology feed-forward neural networks fique fly ash functional adequacy geopolymer green architecture human-centered IEQ in-situ measurements indoor air quality lean construction lean manufacturing learning space mechanical properties MgO-based cement microclimate modular construction natural ventilation nursing homes place attachment quality air social interaction social participation statistics sustainability sustainability architecture sustainable building sustainable materials thermal comfort thermal conductivity thermal insulation thermogravimetry urban environment urban heat island urban models |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566457903321 |
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González Lezcano Roberto Alonso
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical Methods for the Analysis of Genomic Data
| Statistical Methods for the Analysis of Genomic Data |
| Autore | Jiang Hui |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (136 p.) |
| Soggetto topico |
Mathematics and Science
Research and information: general |
| Soggetto non controllato |
Bayes factor
Bayesian mixed-effect model boosting classification classification boundary clustering analysis convolutional neural networks CpG sites deep learning DNA methylation expectation-maximization algorithm false discovery rate control feed-forward neural networks gaussian finite mixture model GEE gene expression gene regulatory network gene set enrichment analysis integrative analysis kernel method lipid-environment interaction longitudinal lipidomics study machine learning multiple cancer types n/a network substructure nonparanormal graphical model omics data Ordinal responses penalized variable selection prognosis modeling RNA-seq uncertainty |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557545803321 |
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Jiang Hui
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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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