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Energies 2018,11, 242
• For theELM, therewere100neurons in thehidden layer,andthehardlimfunctionwaschosenas
theactivationfunctionforconvertingtheoriginal features intoanotherspace.
• For theSVR, thepenaltycoefficientwasset tobe80,andtheradialbasis functionwaschosenas
thekernel functiontorealize thenonlinear transformationof input features.
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Figure 8. Periodicity knowledge and the residual time series of the retail store data set:
(a) thedaily-periodicenergy-consumingpattern; (b) theresidual timeseries.
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Short-Term Load Forecasting by Artificial Intelligent Technologies
- Titel
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Autoren
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2019
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 448
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorie
- Informatik