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Energies2018,11, 1449
The load is influenced by various factors. Here, three variables, including day types,
meteorological conditions and e-bus dispatching, are selected. Unlike traditionalmotor vehicles,
thesourceofpowerforelectricbuses isallelectricpower.Whenthere isa traffic jam, there isnoenergy
loss forelectricbuses. Therefore, trafficcongestionfactorsdonotaffect the loadofchargingstations.
2.1.DayTypes
E-bus charging stations serve the electricity supplyofurbane-buses. In accordancewith the
habitsanddemandsofcitizens, theschedulingofe-busesbetweenweekdaysandweekendsisdifferent
across theweek,whichalsoresults inobviousdifferences in the loadcurve.
Table1displays theannualmeanofdailymaximumloadanddailyaverage loadfor thee-bus
chargingstationinBaodingin2016onthebasisofdaytypes. It canbeseenthat the loadsonworkdays
are relatively higher than those onweekends. Thus, aweek can be divided into two categories,
namelyworkdays, includingMondaytoFriday,andweekends,whichcontainSaturdayandSunday.
Specialholidays, suchasDragonBoatDay,LaborDayorNationalDay, canbeseparatedasanew
typealone.
Table1.Loadcharacteristicsofdifferentdaytypes.
DayType AnnualAverageDailyMaximumLoad/kW AnnualAverageDaily
AverageLoad/kW
Monday 669.16 386.70
Tuesday 663.28 377.07
Wednesday 649.63 376.95
Thursday 647.03 366.23
Friday 636.54 370.55
Saturday 573.46 338.97
Sunday 590.45 349.94
2.2.MeteorologicalConditions
Data related tometeorological conditions and thepower loadof Baoding fromAugust 16 to
September15,2017(31daysintotal)arecollectedandshowninFigure2. Themeteorologicalconditions
includethedailymaximumtemperature,dailyweather,dailyaveragewindspeedanddailyaverage
humidity. In thedailyweathercondition,“1” isusedtorepresentasunnyday,“2” isusedtorepresent
cloudyday,and“3” isusedtorepresentarainyorsnowyday.AscanbeseeninFigure2, there isa
significantpositivecorrelationbetweendailymaximumtemperatureandpower load,andweather
andpower loadshowanegativecorrelation.However, there isnoobviousrelationshipbetweenthe
averagewindspeedfactorandload,andtheaveragehumidity factor is similar. Thus, it canbe found
that the loadofe-buschargingstations is remarkablyaffectedbytemperature,aswellasbyrainyand
snowydays,while the influenceofothermeteorological conditionssuchashumidityandwindspeed
issoweakthat theycanbeomitted. Therefore, temperatureandrainyandsnowydaysareselectedas
influential indicators in thispaper.
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Short-Term Load Forecasting by Artificial Intelligent Technologies
- Title
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Authors
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Editor
- MDPI
- Location
- Basel
- Date
- 2019
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Size
- 17.0 x 24.4 cm
- Pages
- 448
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Category
- Informatik