Spatio-Temporal Data Analytics for Wind Energy Integration
1st Edition
Published on 14. November 2014
VIII, 80 pages
978-3-319-12319-6 (ISBN)
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
VIII, 80 p. 34 illus.
File size
4,19 MB
ISBN-13
978-3-319-12319-6 (9783319123196)
DOI
10.1007/978-3-319-12319-6
Schweitzer Classification
Other editions
Additional editions
Lei Yang | Miao He | Junshan Zhang
Spatio-Temporal Data Analytics for Wind Energy Integration
Book
12/2014
Springer
€53.49
Shipment within 10-15 days
Content
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.
