Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications
dc.contributor.editor | Vitelli, Massimo | |
dc.contributor.editor | Costanzo, Luigi | |
dc.date.accessioned | 2021-05-01T15:42:45Z | |
dc.date.available | 2021-05-01T15:42:45Z | |
dc.date.issued | 2020 | |
dc.identifier | ONIX_20210501_9783039432004_908 | |
dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/69162 | |
dc.description.abstract | This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators. | |
dc.language | English | |
dc.subject.classification | bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology | |
dc.subject.other | sensor network | |
dc.subject.other | data fusion | |
dc.subject.other | complex network analysis | |
dc.subject.other | fault prognosis | |
dc.subject.other | photovoltaic plants | |
dc.subject.other | ANFIS | |
dc.subject.other | statistical method | |
dc.subject.other | gradient descent | |
dc.subject.other | photovoltaic system | |
dc.subject.other | sustainable development | |
dc.subject.other | PV power prediction | |
dc.subject.other | artificial neural network | |
dc.subject.other | renewable energy | |
dc.subject.other | environmental parameters | |
dc.subject.other | multiple regression model | |
dc.subject.other | moth-flame optimization | |
dc.subject.other | parameter extraction | |
dc.subject.other | photovoltaic model | |
dc.subject.other | double flames generation (DFG) strategy | |
dc.subject.other | Solar cell parameters | |
dc.subject.other | single-diode model | |
dc.subject.other | two-diode model | |
dc.subject.other | COA | |
dc.subject.other | photovoltaic systems | |
dc.subject.other | maximum power point tracking | |
dc.subject.other | single stage grid connected systems | |
dc.subject.other | solar concentrator | |
dc.subject.other | spectral beam splitting | |
dc.subject.other | diffractive optical element | |
dc.subject.other | diffractive grating | |
dc.subject.other | PVs power output forecasting | |
dc.subject.other | adaptive neuro-fuzzy inference systems | |
dc.subject.other | particle swarm optimization-artificial neural networks | |
dc.subject.other | solar irradiation | |
dc.subject.other | photovoltaic power prediction | |
dc.subject.other | publicly available weather reports | |
dc.subject.other | machine learning | |
dc.subject.other | long short-term memory | |
dc.subject.other | integrated energy systems | |
dc.subject.other | smart energy management | |
dc.subject.other | PV fleet | |
dc.subject.other | clustering-based PV fault detection | |
dc.subject.other | unsupervised learning | |
dc.subject.other | self-imputation | |
dc.subject.other | implicit model solution | |
dc.subject.other | photovoltaic array | |
dc.subject.other | series–parallel | |
dc.subject.other | global optimization | |
dc.subject.other | partial shading | |
dc.subject.other | deterministic optimization algorithm | |
dc.subject.other | metaheuristic optimization algorithm | |
dc.subject.other | genetic algorithm | |
dc.subject.other | solar cell optimization | |
dc.subject.other | finite difference time domain | |
dc.subject.other | optical modelling | |
dc.subject.other | thermal image | |
dc.subject.other | photovoltaic module | |
dc.subject.other | hot spot | |
dc.subject.other | image processing | |
dc.subject.other | deterioration | |
dc.subject.other | linear approximation | |
dc.subject.other | MPPT algorithm | |
dc.subject.other | duty cycle | |
dc.subject.other | global horizontal irradiance | |
dc.subject.other | mathematical modeling | |
dc.subject.other | feed-forward neural networks | |
dc.subject.other | recurrent neural networks | |
dc.subject.other | LSTM cell | |
dc.subject.other | performances evaluation | |
dc.subject.other | clear sky irradiance | |
dc.subject.other | persistent predictor | |
dc.subject.other | photovoltaics | |
dc.subject.other | artificial neural networks | |
dc.subject.other | national power system | |
dc.title | Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications | |
dc.type | book | |
oapen.identifier.doi | 10.3390/books978-3-03943-201-1 | |
oapen.relation.isPublishedBy | 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 | |
oapen.relation.isbn | 9783039432004 | |
oapen.relation.isbn | 9783039432011 | |
oapen.pages | 280 | |
oapen.place.publication | Basel, Switzerland |
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