Spatio-Temporal Data Analytics for Wind Energy Integration Paperback / softback
by Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
Part of the SpringerBriefs in Electrical and Computer Engineering series
Paperback / softback
Description
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.
Information
-
Item not Available
- Format:Paperback / softback
- Pages:80 pages, 15 Tables, black and white; 34 Illustrations, black and white; VIII, 80 p. 34 illus.
- Publisher:Springer International Publishing AG
- Publication Date:15/11/2014
- Category:
- ISBN:9783319123189
Other Formats
- PDF from £38.24
Information
-
Item not Available
- Format:Paperback / softback
- Pages:80 pages, 15 Tables, black and white; 34 Illustrations, black and white; VIII, 80 p. 34 illus.
- Publisher:Springer International Publishing AG
- Publication Date:15/11/2014
- Category:
- ISBN:9783319123189
