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Spatio-Temporal Data Analytics for Wind Energy Integration

  • Lei Yang
  • Miao He
  • Junshan Zhang
  • Vijay Vittal

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 1-6
  3. Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 7-34
  4. Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 35-57
  5. Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 59-75
  6. Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
    Pages 77-80

About this book

Introduction

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.

Keywords

Distributional forecast Economic dispatch Graphical learning Markov chains Point forecast Short-term wind power forecast Spatio-temporal analysis Stochastic optimization Support vector machines Wind farm

Authors and affiliations

  • Lei Yang
    • 1
  • Miao He
    • 2
  • Junshan Zhang
    • 3
  • Vijay Vittal
    • 4
  1. 1.Electrical Computer & Energy Engineering Ira A. Fulton School of EngineeringArizona State UniversityTempeUSA
  2. 2.Department of Electrical and Computer EngineeringTexas Tech UniversityLubbockUSA
  3. 3.Electrical Computer & Energy Engineering Ira A. Fulton School of EngineeringArizona State UniversityTempeUSA
  4. 4.Electrical Computer & Energy Engineering Ira A. Fulton School of EngineeringArizona State UniversityTempeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-12319-6
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, Cham
  • eBook Packages Energy
  • Print ISBN 978-3-319-12318-9
  • Online ISBN 978-3-319-12319-6
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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