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Energies2018,11, 1678
beingmorethan96.7MWtoolow.Theseextremeerrorscanapproach20%of themeanheat loadin
December.
Figure5.Performanceof theSVRmodelontheyear2016,usingrealweather forecasts, calendar,and
holidaydata. Threedifferenterrormetricsareshownforeachmonthof theyear. The forecasterror
varieswith the timeofday, shownonthehorizontalaxes. RMSE(blue)andMAE(yellow)areshown
unitsofMWonthe leftaxes.MAPE(red) is showninpercentontherightaxes.
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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