Spatio-Temporal Data Analytics for Wind Energy Integration (SpringerBriefs in Electrical and Computer Engineering)
暫譯: 風能整合的時空數據分析(電氣與計算機工程系列)

Lei Yang

  • 出版商: Springer
  • 出版日期: 2014-12-03
  • 售價: $2,420
  • 貴賓價: 9.5$2,299
  • 語言: 英文
  • 頁數: 88
  • 裝訂: Paperback
  • ISBN: 3319123181
  • ISBN-13: 9783319123189
  • 相關分類: Data Science
  • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

本書 SpringerBrief 介紹了使用隨機建模和優化方法進行風能整合的時空數據分析。它探討了將可再生能源發電有效整合到大規模電力網絡中的技術。風能的運營挑戰及其變異性被仔細檢視。時空分析方法使作者能夠開發基於馬可夫鏈的風電場發電短期預測。為了處理風能的波動動態,提出了一種增強型馬可夫模型的支持向量機。此外,還研究了經濟調度(ED)和可中斷負載管理的隨機優化。 《風能整合的時空數據分析》對於致力於可再生能源整合的研究人員和專業人士具有重要價值。學習電氣、計算機和能源工程的高級學生也應該會發現這些內容有用。