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Spatio-Temporal Data Analytics for Wind Energy Integration - Junshan Zhang,Lei Yang,Miao He,Vijay Vittal

englanti
2014-12-03
60,05 € 92,38 €

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Toimitus 12-18 arkipäivässä

30 päivän palautusoikeus

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 sho ... Täydellinen kuvaus

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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 (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful.

Lisätietoja

Kirjoittaja Junshan Zhang, Lei Yang, Miao He, Vijay Vittal
Julkaisija Springer Nature Switzerland
Series SpringerBriefs in Electrical and Computer Engineering
Julkaisuvuosi 2014
Kannen tyyppi Pehmeäkantinen
EAN 9783319123189
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Arvostelet: Spatio-Temporal Data Analytics for Wind Energy Integration
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60,05 € 92,38 €