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Energies2018,11, 2038
agreedwith theretailer (whichrefers to thevalueof theenergyconsumed).Note that theconcepts
corresponding toaccess fees (power andenergy terms) and taxes are independent of themodeof
supply, so theydonot change for aDirectConsumer. Therefore, the calculationof the cost of the
referenced energy for theDMCand its comparisonwith the retailer cost, is themain concern for
this study.
Recall that,under theassumptionsof this study, thehourlyenergycostasaDMCisgivenbythe
sumof fourcomponents: thecost in theDailyMarket (DMcost), theadjustmentservices (AScost),
themeasureddeviations (MDcost), and thecapacitypayments (CPcost). Table9 shows thevalue
of eachcomponentwhen thecostof theenergyasaDMCisevaluated. In this section, 48-h-ahead
predictionsobtainedwith theXGBoostmethod(eta=0.02,nrounds=3700)wereused,althoughany
of theotherensemblemethodswould leadtosimilar results. It isworth tomention that thecostof
deviations isquite limiteddueto theaccuracyof the loadforecastingmethod.
Table9.MonthlycostactingasaDirectMarketConsumers (DMC)anditscomponents.
Month DMCost (inā¬) ASCost (inā¬) MDCost (inā¬) CPCost (inā¬) DMCCost (inā¬)
January 5478 685 91 313 6567
February 4492 815 56 409 5772
March 3644 763 45 99 4551
April 2980 649 70 105 3804
May 3976 801 43 127 4948
June 6692 682 42 336 7752
July 7151 610 28 524 8313
August 4450 430 56 0 4936
September 8013 724 57 195 8989
October 8289 708 48 130 9176
November 7960 474 91 140 8665
December 8727 492 46 285 9575
Total2016 71,853 (86.52%) 7834 (9.44%) 697 (0.84%) 2664 (3.2%) 83,048
Table10showstheelectricityconsumption (inkWh)of thecampusuniversity in2016andthe
costof the referencedenergy (consumption) in fourcases: the real costpaid to the retailer, thecost
usingtheAverageFinalPrice (AFP),actingasaDMC,andwhatwecall thepessimistprice (aDirect
Consumerwithall thedeviationsagainst thesystem).Accordingto theresults, it canbeestablished
thatDMCmodalitywouldhaveproducedsavingsofaround11%intheenergytermof the invoice
whencomparedto theretailprice.Notealso that thecostusingtheAFPdoesnotcoincidewith the
costof theDMCbecause thecostdue todeviationsandthecapacitypaymentscomponentsdependon
theconsumer.Ontheotherhand, theresults showthat, even in thepessimistic case (alldeviationsof
thepredictionsagainst thesystem), theDMCtypeofsupply isworthyagainst theretailer.
It is important tohighlight that the economicbeneļ¬ts of theDMCtypeof supplydependon
twomainaspects: themagnitudeof thedeviationsand thedirectionof thedeviations (towardsor
against thesystem). Theļ¬rstaspect (magnitudeof thedeviations) isdeterminedbytheaccuracyof the
forecastingmethod.However, thesecondaspect (directionof thedeviations) isoutofourcontroland
itdependsonthewholeElectricSystem. Inparticular, someworse forecastingmethodscould leadto
greaterbeneļ¬ts thanmoreaccuracymethods,butonlybychanceandassumingthat the forecasting
valuesaregoodenough(moderatedeviations). Therefore, the loadforecastingmethodis important to
someextent,butobviously lowerdeviationsarepreferable togreaterdeviations.
175
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