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Taschenbuch. Zustand: Neu. Study of the stochastic and cyclic behavior of wind in Algeria | Intended for wind energy applications, desertification control and environmental protection | Farouk Chellali | Taschenbuch | Englisch | 2021 | Our Knowledge Publishing | EAN 9786204303192 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: Our Knowledge Publishing Nov 2021, 2021
ISBN 10: 6204303198 ISBN 13: 9786204303192
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Wind is a complex atmospheric phenomenon that can be studied in multiple ways at different scales. In this work, we propose to study the stochastic and cyclic behavior of wind in Algeria. The analysis of the problem is structured in three parts. The first part consists in modeling the probability density of the wind while exploiting the maximum entropy approach. The comparison between the statistical performances of this approach and the conventional Weibull distribution allowed to deduce that the proposed approach is better in terms of mean square error. In a second study, we studied the cyclic behavior of the wind using time-frequency analysis in order to follow the variations of the wind spectral container with respect to time. In a third study, we predict the short-term wind speed using self-recursive mean-fitting models as well as models based on neural network theory. 96 pp. Englisch.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Wind is a complex atmospheric phenomenon that can be studied in multiple ways at different scales. In this work, we propose to study the stochastic and cyclic behavior of wind in Algeria. The analysis of the problem is structured in three parts. The first p.
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Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
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Sprache: Englisch
Verlag: Our Knowledge Publishing Nov 2021, 2021
ISBN 10: 6204303198 ISBN 13: 9786204303192
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Wind is a complex atmospheric phenomenon that can be studied in multiple ways at different scales. In this work, we propose to study the stochastic and cyclic behavior of wind in Algeria. The analysis of the problem is structured in three parts. The first part consists in modeling the probability density of the wind while exploiting the maximum entropy approach. The comparison between the statistical performances of this approach and the conventional Weibull distribution allowed to deduce that the proposed approach is better in terms of mean square error. In a second study, we studied the cyclic behavior of the wind using time-frequency analysis in order to follow the variations of the wind spectral container with respect to time. In a third study, we predict the short-term wind speed using self-recursive mean-fitting models as well as models based on neural network theory.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Wind is a complex atmospheric phenomenon that can be studied in multiple ways at different scales. In this work, we propose to study the stochastic and cyclic behavior of wind in Algeria. The analysis of the problem is structured in three parts. The first part consists in modeling the probability density of the wind while exploiting the maximum entropy approach. The comparison between the statistical performances of this approach and the conventional Weibull distribution allowed to deduce that the proposed approach is better in terms of mean square error. In a second study, we studied the cyclic behavior of the wind using time-frequency analysis in order to follow the variations of the wind spectral container with respect to time. In a third study, we predict the short-term wind speed using self-recursive mean-fitting models as well as models based on neural network theory.