Spatio-Temporal Data Analytics for Wind Energy Integration (SpringerBriefs in Electrical and Computer Engineering)

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.

這本SpringerBrief書籍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型電力網格中的技術。仔細研究了風能的運營挑戰和其變動性。時空分析方法使作者能夠開發基於馬爾可夫鏈的風場發電短期預測模型。為了應對風力爬坡動態，引入了支持向量機增強的馬爾可夫模型。同時，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非常有價值。學習電氣、計算機和能源工程的高級學生也應該會發現這本書的內容有用。