Spatio-Temporal Data Analytics for Wind Energy Integration by Lei YangSpatio-Temporal Data Analytics for Wind Energy Integration by Lei Yang

Spatio-Temporal Data Analytics for Wind Energy Integration

byLei Yang, Miao He, Junshan Zhang

Paperback | December 3, 2014

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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.
Title:Spatio-Temporal Data Analytics for Wind Energy IntegrationFormat:PaperbackDimensions:80 pagesPublished:December 3, 2014Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319123181

ISBN - 13:9783319123189

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Table of Contents

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.