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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2019,12, 57 Threestandardloadprofiles [43,44],as illustrated inFigure5,wereappliedto the loadmodule to investigate the impacts of loads on the optimization of the hybrid power system. The 61-day historicaldatawereusedforsimulationandoptimizationanalyses. Table3 illustrates thestatistical dataof these loadprofiles,where thehouseholdhadthe largesthistoricallypeakandtheofficehadthe largestdailyaveragepeak,while the laboratory loadhadthegreatestenergyconsumption. Therefore, weusedthese three typical loads todemonstratehowloadcharacteristicscanaffect theperformance optimizationof thehybridpowersystem. ȱ (a)ȱ61Ȭdayȱhistoricalȱdataȱ (b)ȱDailyȱaverageȱ[43,44]ȱ WLPH KU 8VHU /RDG DYHUDJH +RXVH 2IILFH /DE Figure5.Threestandard loadprofiles. Table3.Thestatisticaldataof loadprofiles [39]. Household Lab Office Historicpeak(W) 6220 3395 5333 Dailyaveragepeak(W) 1237 1811 2178 Dailyaverage (kWh) 19.96 30.41 22.32 3.DesignOptimizationof theHybridPowerSystem Thehybridpowermodelwasapplied topredict systemresponsesunderdifferent conditions, suchastheuseofvaryingcomponentsandloads.Wedefinedthreeindexestoevaluatetheperformance of thehybridpowersystem: cost, reliability,andsafety,asdescribedbythe following: (1) Systemcost: thesystemcost J(b, s,w) consistedof twoparts, Ji and Jo, as follows[39]: J(b, s,w) = Ji(b, s,w) + Jo(b, s,w) (2) where Jiand Jo indicatetheinitialandoperationcosts, respectively. Thesubscriptsb,s,andwrepresent thenumbersofbatteries,PVarrays,andWTsinunitsof100Ah,1kW,and3kW,respectively. The initial cost Jiaccountedfor the investment in thecomponents, suchas thePEMFC,powerelectricdevices, PVarrays,WT,hydrogenelectrolyzer, chemicalhydrogengenerator,andbatteryset, as follows: Ji(b, s,w) =∑kJki(b, s,w) (3) wherek=PEMFC,DC,solar,WT,HE,CHG,andbatt, respectively. Theoperationcost Jo includedthehydrogenconsumptionandthemaintenanceof theWTandPV arrays,as in the following: Jo(b, s,w) =∑ lJlo(b, s,w) (4) where l=NaBH4,WT,andsolar, respectively. 88
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies