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Energies2018,11, 1893
time. Thiscouldhelp tomodel thechangesofclustersalongtimebutwehave to thinkaboutapenalty
mechanismallowingtomakechanges in theclusteronlywhenit isuseful.
AuthorContributions:B.Auder, J.Cugliari,Y.GoudeandJ.-M.Poggiequallycontributedto thiswork.
Acknowledgments: This research benefited from the support of the FMJH ’Program Gaspard Monge for
optimization and operations research and their interactionswith data science’, and from the support from
EDFandThales.
Conflictsof Interest:Theauthorsdeclarenoconflictof interest.
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248
Short-Term Load Forecasting by Artificial Intelligent Technologies
- Title
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Authors
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Editor
- MDPI
- Location
- Basel
- Date
- 2019
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Size
- 17.0 x 24.4 cm
- Pages
- 448
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Category
- Informatik