Spatio-Temporal Data Analytics for Wind Energy Integration (SpringerBriefs in Electrical and Computer Engineering) [9783319123189]

Overview

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.

The book, Spatio-Temporal Data Analytics for Wind Energy Integration (SpringerBriefs in Electrical and Computer Engineering) [Bulk, Wholesale, Quantity] ISBN#9783319123189 in Paperback by Lei Yang, Miao He, Junshan Zhang, Vijay Vittal may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.