Spatio-Temporal Data Analytics for Wind Energy Integration

• Κύριοι συγγραφείς: Yang, Lei (Συγγραφέας), He, Miao (Συγγραφέας), Zhang, Junshan (Συγγραφέας), Vittal, Vijay (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
• Σειρά: SpringerBriefs in Electrical and Computer Engineering,
• Θέματα: Energy.; Renewable energy resources.; Energy policy.; Energy and state.; Data mining.; Electric power production.; Renewable energy sources.; Alternate energy sources.; Green energy industries.; Renewable and Green Energy.; Data Mining and Knowledge Discovery.; Energy Policy, Economics and Management.; Energy Technology.

 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. 

 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.