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Energy market players (investors, power producers, grid operators, consumers, etc.) are facing potential challenges such as the growing demand for energy, new patterns of energy consumption, the integration of (intermittent) renewable energy sources into power grids and the evolution of power grids.This book investigates the possibility of predicting the production of a self-consuming photovoltaic installation by artificial neural networks. We cross-compared two neural network architectures (looped and unlooped) with respect to multivariate regression in order to have an efficient and reliable tool for predicting the production of a PV installation based on meteorological data (sunshine and ambient temperature).To do so, we used monitoring data of a plant over a 72-day period to build, train and test two neural network topologies (looped and unlooped) which are trained with the Levenberg-Marquardt algorithm.
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Predicting the output of a PV plant
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ISBN 10: 6203354201
ISBN 13: 9786203354201
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Predicting the output of a PV plant
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ISBN 10: 6203354201
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Predicting the output of a PV plant
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Energy market players (investors, power producers, grid operators, consumers, etc.) are facing potential challenges such as the growing demand for energy, new patterns of energy consumption, the integration of (intermittent) renewable energy sources into power grids and the evolution of power grids.This book investigates the possibility of predicting the production of a self-consuming photovoltaic installation by artificial neural networks. We cross-compared two neural network architectures (looped and unlooped) with respect to multivariate regression in order to have an efficient and reliable tool for predicting the production of a PV installation based on meteorological data (sunshine and ambient temperature).To do so, we used monitoring data of a plant over a 72-day period to build, train and test two neural network topologies (looped and unlooped) which are trained with the Levenberg-Marquardt algorithm. 64 pp. Englisch. Seller Inventory # 9786203354201
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Predicting the output of a PV plant
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Our Knowledge Publishing, 2021
ISBN 10: 6203354201
ISBN 13: 9786203354201
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Predicting the output of a PV plant : Application of artificial neural networks
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ISBN 10: 6203354201
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Energy market players (investors, power producers, grid operators, consumers, etc.) are facing potential challenges such as the growing demand for energy, new patterns of energy consumption, the integration of (intermittent) renewable energy sources into power grids and the evolution of power grids.This book investigates the possibility of predicting the production of a self-consuming photovoltaic installation by artificial neural networks. We cross-compared two neural network architectures (looped and unlooped) with respect to multivariate regression in order to have an efficient and reliable tool for predicting the production of a PV installation based on meteorological data (sunshine and ambient temperature).To do so, we used monitoring data of a plant over a 72-day period to build, train and test two neural network topologies (looped and unlooped) which are trained with the Levenberg-Marquardt algorithm. Seller Inventory # 9786203354201
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Predicting the output of a PV plant
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Predicting the output of a PV plant
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ISBN 10: 6203354201
ISBN 13: 9786203354201
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Energy market players (investors, power producers, grid operators, consumers, etc.) are facing potential challenges such as the growing demand for energy, new patterns of energy consumption, the integration of (intermittent) renewable energy sources into power grids and the evolution of power grids.This book investigates the possibility of predicting the production of a self-consuming photovoltaic installation by artificial neural networks. We cross-compared two neural network architectures (looped and unlooped) with respect to multivariate regression in order to have an efficient and reliable tool for predicting the production of a PV installation based on meteorological data (sunshine and ambient temperature).To do so, we used monitoring data of a plant over a 72-day period to build, train and test two neural network topologies (looped and unlooped) which are trained with the Levenberg-Marquardt algorithm.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Seller Inventory # 9786203354201
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Predicting the output of a PV plant | Application of artificial neural networks
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Taschenbuch. Condition: Neu. Predicting the output of a PV plant | Application of artificial neural networks | Abdou Aziz Cissé (u. a.) | Taschenbuch | Englisch | 2021 | Our Knowledge Publishing | EAN 9786203354201 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 119741851