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Energies2018,11, 1282 Table1.KMOandBarlett testof sphericity. KMO Value 0.740 Barlett testof sphericity Approximatechi-squarevalue 1525.304 Degreesof freedom(Df.) 231 Significance (Sig.) 0.000 Table2.Resultsof factoranalysis. Indicator Variable Load ContributionRate (%) Factor1 Minimumtemperature 0.732 35.128 Dailymaximumload 0.714 Dailyminimumload 0.726 Averagedaily load 0.870 Seasonpatterns 0.736 Peakaverage loadofpreviousday 0.922 Valleyaverage loadofpreviousday 0.801 Average loadof thedaybefore 0.917 Average loadof2daysbefore 0.830 Average loadof3daysbefore 0.695 Factor2 Maximumtemperature āˆ’0.732 19.646 Average temperature āˆ’0.697 Humidity 0.810 Visibility āˆ’0.724 Weatherpatterns 0.724 Average loadof4daysbefore 0.547 Factor3 Typeofdate 0.622 10.514Average loadof5daysbefore 0.612 Average loadof6daysbefore 0.609 Factor4 Airpressure 0.563 7.746 Factor5 Date 0.883 6.087 Factor6 Windspeed āˆ’0.533 5.313 3.3. TheAnalysis ofCorrelation Additionally, this paper conducted a further analysis of the correlation between the amount ofhistorical loadand the target load fromtwodifferentviewpoints soas to eliminate the internal correlation.Ontheonehand, thepartialautocorrelationfunction(PACF)wascarriedout throughout the overall power load to dig out the correlation between the target load and the previous load. Ontheotherhand, thewhole loaddatawith thesametimeintervalwerealso implementedbyPACF individually toseektherelationshipamongthe loadwiththesametime. Theresultsofpartialauto correlationcanbeseen inFigures4and5, respectively. For instance,under theconfidence levelof90%, it canbeseenfromFigure4 that the lagsof the first2haresignificant to thecurrentdata. That is tosay, the loadsof thefirst twohoursare influential to thecurrent load.As forFigure5, it isknownthatonly thefirst lag1 isprominent to thecurrent load dataexcept the loadof00:00 (Lag2). Consequently, it canbeconcludedthat the four factors including thefirst twohoursbefore00:00andthesametimepower loadthatoccurredyesterdayandtheday beforeyesterdaywereselectedas the input factorsat the timeof00:00. 343
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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies