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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1253 Figure7.Relationshipbetweendaytypeanddaily loadofEVchargingstation. 4.CaseStudy TheselectedEVchargingstation in thispaper iscomposedof5 largepowerchargerswhichcan beusedbybatterieswithhighcapacity inasinglebox,orseriesbatterieswith lowcapacity in three boxes, and10smallpowerchargers that canbeonlyemployedbyabatterywith lowcapacity ina singlebox. The loaddataevery30minfrom1June2016to30November2017arecollectedfromthe chargingstation. Thedata from1June2016 to29November2017areselectedas trainingset, andthe remainingdataon30November2017areutilizedas test set. 4.1. InputSelectionandProcessing Accordingto theanalysisof the loadcharacteristics forEVchargingstation, ten influential factors includingseasonalcategory,maximumtemperature,minimumtemperature,weathercondition,day type,andthe loadsat thesamemoment in thepreviousfivedaysareselectedas input in thispaper. The input featuresarediscussedas follows: (a) theseasoncanbedividedinto fourcategories: spring (March,AprilandMay), summer(June, JulyandAugust), autumn(September,October,November) andwinter (December, JanuaryandFebruary),whicharesetas{1,2,3,4}. (b)Weatherconditionsare decomposedinto twotypes: sunnyandcloudydays,valuedat1,andrainyandsnowydays,valuedat 0.5. (c)Dayscanbedivided intoworkdays (MondaytoFriday)andweekends (SaturdayandSunday). Whenquantifying thedaytype,workdaysarevaluedat1,andweekendsat0.5. Because thecollecting data isnotpublicallyavailable, statisticallysignificantparametersarepresented inTable1. Table1.Statisticallysignificantparametersof thecollectingdata. Statistics TotalDays MaximumLoad(MW) MinimumLoad (MW) Maximum Temperature (◦C) MinimumTemperature (◦C) Value 547 5.212 0.006 36 −13 Statistics Numberofdaysinspring(day) Numberofdays insummer(day) Numberofdays inautumn(day) Numberofdays in winter (day) Numberofprecipitation days (day) Value 92 184 182 89 76 Thetemperatureandloaddatashouldbenormalized inaccordancewithEquation(11). Y={yi}= xi−xminxmaxxmin i=1,2,3, . . . ,n (11) 363
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies