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Spatio-Temporal Data Analytics for Wind Energy Integration

Jezik EngleskiEngleski
Knjiga Meki uvez
Knjiga Spatio-Temporal Data Analytics for Wind Energy Integration Lei Yang
Libristo kod: 06841837
Nakladnici Springer International Publishing AG, studeni 2014
This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochas... Cijeli opis
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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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Informacije o knjizi

Puni naziv Spatio-Temporal Data Analytics for Wind Energy Integration
Autor Lei Yang, Miao He, Junshan Zhang, Vijay Vittal
Jezik Engleski
Uvez Knjiga - Meki uvez
Datum izdanja 2014
Broj stranica 80
EAN 9783319123189
ISBN 3319123181
Libristo kod 06841837
Nakladnici Springer International Publishing AG
Težina 1474
Dimenzije 155 x 235 x 235

Kategorija

Mathematics & Science > Physics > Classical mechanics > Energy
Computing & Information Technology > Computer science > Artificial intelligence > Expert systems / knowledge-based systems
Economics, Finance, Business & Management > Industry & industrial studies > Energy industries & utilities
Technology, Engineering, Agriculture > Energy technology & engineering > Alternative & renewable energy sources & technology
Computing & Information Technology > Databases > Data mining
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