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Energies2018,11, 2080
discardedaseitherhotor cold. Alldays thatdonotbelong tooneof thecategories (special, hotor
cold)areconsideredasregulardays.
3.DataAnalysis
It is important todescribe thecharacteristicsof thedataseries relevant to the forecastingprocess
inorder tounderstand the forecastingproblemandwhetherornot its conclusionsmayapply toa
differentcase:
3.1. Load
The load data series covers from 2010 to 2017 and it includes hourly values of electricity
consumption in theSpanish inlandsystem. The long-termtrendof the series shown inFigure5 is
related toeconomicgrowth,efficiency improvementsandbehavioral shifts like theuseofACsystems.
Onashorter termscale, the factorsdrivingthe loadinSpainare temperatureandsocialevents
andholidays,whichareexplainedin the followingsubsections.
2004 2006 2008 2010 2012 2014 2016 2018
0
0.2
0.4
0.6
0.8
1 Evolution of national electricity demand and Gross National Product
Normalized GNP
Normalized load
Figure5. Evolutionof 52weeksmovingaverage loadandGrossNationalProduct. Both series are
normalized[0,1].
3.2. Temperature
The temperature data available includes series from59 stations scattered across the country.
Realdataofdailymaximumandminimumdata iscollectedalongwithdaily forecastsofupto ten
daysahead. Therefore, it ispossible tosimulatereal timeconditions if forecastsareusedinsteadof
realdata.
As it was explained before, the national forecast only uses information from five stations
selectedfromthe59available. This selection ismadethroughanempiricalevaluation. Inaddition,
the temperature fromupto fourpreviousdays isalsoused inorder tocapture thedynamicsof the
temperature-load relation. Thenon-linearity of the relation ismodeledusing theCDDandHDD
approachalreadydiscussed. Figure6showsthescatterplotofnational loadat18honweekdayagainst
temperatureat the threemost relevant locations. TheHDDandCDDlinearization isalsoplottedfor
each locationalongwith theMeanAveragePercentageError (MAPE)betweentheactual loadandthe
linearizedone.
147
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