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Record Nr. |
UNINA9910299626403321 |
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Autore |
Yang Lei |
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Titolo |
Spatio-Temporal Data Analytics for Wind Energy Integration / / by Lei Yang, Miao He, Junshan Zhang, Vijay Vittal |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (86 p.) |
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Collana |
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SpringerBriefs in Electrical and Computer Engineering, , 2191-8112 |
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Disciplina |
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Soggetti |
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Renewable energy resources |
Data mining |
Energy policy |
Energy and state |
Energy systems |
Renewable and Green Energy |
Data Mining and Knowledge Discovery |
Energy Policy, Economics and Management |
Energy Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation -- Support Vector Machine Enhanced Markov Model for Short-Term Wind Power Forecast -- Stochastic Optimization based Economic Dispatch and Interruptible Load Management -- Conclusions and Future Works. |
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Sommario/riassunto |
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This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch |
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