| 書目名稱 | Hybrid Intelligent Technologies in Energy Demand Forecasting |
| 編輯 | Wei-Chiang Hong |
| 視頻video | http://file.papertrans.cn/431/430109/430109.mp4 |
| 概述 | Describes the most advanced and accurate energy demand forecasting models.Demonstrates how cutting-edge hybrid intelligent technologies can be combined with traditional models.Includes a wealth of exa |
| 圖書封面 |  |
| 描述 | .This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies.?.It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory.?.The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.. |
| 出版日期 | Book 2020 |
| 關鍵詞 | Support Vector Regression; Energy Demand Forecasting; Meta-Heuristic Algorithms; Chaotic Mapping Mechan |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-36529-5 |
| isbn_softcover | 978-3-030-36531-8 |
| isbn_ebook | 978-3-030-36529-5 |
| copyright | Springer Nature Switzerland AG 2020 |
|Archiver|手機版|小黑屋|
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