| 书目名称 | Spatio-Temporal Data Analytics for Wind Energy Integration |
| 编辑 | Lei Yang,Miao He,Vijay Vittal |
| 视频video | |
| 概述 | Includes supplementary material: |
| 丛书名称 | SpringerBriefs in Electrical and Computer Engineering |
| 图书封面 |  |
| 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. |
| 出版日期 | Book 2014 |
| 关键词 | Distributional forecast; Economic dispatch; Graphical learning; Markov chains; Point forecast; Short-term |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-12319-6 |
| isbn_softcover | 978-3-319-12318-9 |
| isbn_ebook | 978-3-319-12319-6Series ISSN 2191-8112 Series E-ISSN 2191-8120 |
| issn_series | 2191-8112 |
| copyright | The Author(s) 2014 |
发表于 2025-3-21 18:55:36
