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Energies2018,11, 1948
Funding:This researchwasfundedbytheMinistryofScienceandTechnology,R.O.C., inTaiwanunderGrands
MOST105-2622-E-002-029 -CC3,MOST106-2622-E-002-028 -CC3,andMOST106-2221-E-002-165-. This research
wasalso supported inpartby theMinistryofScienceandTechnologyofTaiwan (MOST107-2634-F-002-018),
NationalTaiwanUniversity,Center forArtificial Intelligence&AdvancedRobotics. Theauthorswouldlike to
thankM-FieldTM for theircollaborationandtechnical supports.
Acknowledgments: Thisworkwasfinancially supported inpart by theMinistry of Science andTechnology,
R.O.C., in Taiwan under Grands MOST 104-2622-E-002-023 -CC3, MOST 104-2221-E-002-086-, and MOST
105-2622-E-002-029 -CC3. The authors would like to thank M-FieldTM for their collaboration and
technical supports.
Conflictsof Interest:Theauthorsdeclarenoconflictof interest.
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209
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