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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Spatio-Temporal Data Analytics for Wind Energy Integration

Spatio-Temporal Data Analytics for Wind Energy Integration

Yang, Lei
He, Miao
Zhang, Junshan

Paperback

Series: Springerbriefs in Electrical and Computer Engineering

Business GeneralDatabasesGeneral Science

ISBN10: 3319123181
ISBN13: 9783319123189
Publisher: Springer Nature
Published: Dec 3 2014
Pages: 80
Weight: 0.30
Height: 0.19 Width: 6.14 Depth: 9.21
Language: English
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.

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