Al-Driven Intelligent Optimization and Synergistic Integration of Multi-Energy Systems (Wind Energy Engineering)

個数：

予約

Al-Driven Intelligent Optimization and Synergistic Integration of Multi-Energy Systems (Wind Energy Engineering)

Tian, Zhuang (EDT)/ Zhou, Daming (EDT)

ウェブストア価格 ¥47,012（本体¥42,739）
Academic Press Inc（2026/08発売）
外貨定価 US$ 210.00
ポイント 427pt

現在予約受付中です。出版後の入荷・発送となります。
（重要：表示されている発売日は予定となり、発売が延期、中止、生産限定品で商品確保ができないなどの理由により、ご注文をお取消しさせていただく場合がございます。予めご了承ください。）

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 350 p.
言語 ENG
商品コード 9780443445521

Full Description

Al-Driven Intelligent Optimization and Synergistic Integration of Multi-Energy Systems introduces advanced artificial intelligence methods in the operations, management, and performance of renewable energy systems, focusing on wind energy, solar energy, and hydrogen systems. The book addresses key problems such as low accuracy and efficiency in traditional wind power system modeling and control, challenges in wind energy resource prediction and intelligent scheduling, intelligent fault management of wind and hydrogen energy systems, complex scheduling and energy management challenges of multi-energy complementary systems when connected to the grid, and scheduling and reliability challenges in multi-energy system grid connection. Real-world applications and case studies are used throughout to help readers integrate academic research with practical engineering applications, to enhance energy system design and management. This latest volume in the Elsevier Wind Energy Engineering Series is of interest to all those who are interested in the integration of AI in the operation of complex energy systems, including researchers, students, faculty, engineers, practitioners, and policy makers.

1. Artificial Intelligence in Wind Energy Systems and Future Energy Ecology. Frontiers and Synergies
2. Adaptive Control Methods of Intelligent Control Theory in Wind Energy Systems
3. Data-Driven Intelligent Forecasting and Optimization Scheduling of Wind Energy Resources
4. Intelligent Fault Diagnosis and Strategies for Hydrogen Energy Systems
5. Intelligent Energy Management and Fault Prevention in Multi-Energy Complementary Microgrids
6. Collaborative Optimization and Management Strategies in Distributed Multi-Energy Systems
7. AI-Based Multi-Energy Grid Management and Load Control Strategies
8. Intelligent Scheduling and Optimization Technologies in Multi-Energy Grid Systems
9. Coordinated Scheduling and Fault Recovery Mechanisms in Dynamic Power Grids
10. Conclusion. The Vision and Challenges of Intelligent Energy Futures