Spatio-Temporal Data Analytics for Wind Energy Integration
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Spatio-Temporal Data Analytics for Wind Energy Integration “
- 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
Bibliographische Angaben
- Autoren: Lei Yang , Miao He , Junshan Zhang , Vijay Vittal
- 2014, 2014, VIII, 80 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319123181
- ISBN-13: 9783319123189
- Erscheinungsdatum: 03.12.2014
Sprache:
Englisch
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