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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 Hydrogen fuel canbeobtained fromtwosources forPEMFCoperation: chemicalproduction andelectrolysis. As thecostsofdifferent energysourcesandstoragearenot the same,weutilized standard load profiles, irradiance, andwind data, as shown in Figure 7, for the simulation and optimizationanalyses. (a) Load profiles. (b) Irradiance (c) Wind speed 7LPH KU 8VHU /RDG $YHUDJH 2IILFH /DE +RXVHKROG 7LPH KU 6RODU SURILOH $YHUDJH ,UUDGLDQFH [ 7LPH V ᖼ喽:LQG SURILOH $YHUDJH Figure7.Dailyaveragedata. The system responses can be applied to evaluate the system cost and reliability under differentconditions. Thesystemcost J(b,s,w)wasdefinedas follows[4]: J(b,s,w) = Ji(b,s,w)+ Jo(b,s,w) (5) where Ji and Jo were the initial and operation costs, respectively, of the hybrid power system. In Equation (5), b, s, andw represent the numbers of the battery, PV array, andWT in units of 202
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies