Spatio-Temporal Data Analytics for Wind Energy Integration

Nonfiction, Science & Nature, Science, Physics, Energy, Computers, Database Management, Technology
Cover of the book Spatio-Temporal Data Analytics for Wind Energy Integration by Lei Yang, Miao He, Junshan Zhang, Vijay Vittal, Springer International Publishing
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Author: Lei Yang, Miao He, Junshan Zhang, Vijay Vittal ISBN: 9783319123196
Publisher: Springer International Publishing Publication: November 14, 2014
Imprint: Springer Language: English
Author: Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
ISBN: 9783319123196
Publisher: Springer International Publishing
Publication: November 14, 2014
Imprint: Springer
Language: English

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.

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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.

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