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Energies2018,11, 3442
values that is, thedifferencebetweentheactualandmeanandthedifferencebetweentheestimated
andthemeangives theR2value.
1−R2={SSR/SST}
SSRrefers to theresidual sumofsquaresandSSTrefers to the total sumofsquares.
The standarderrorof theestimate is thedistancebetween theestimatedand theactualvalue.
Theconstantvalue isactually the ‘y’ interceptof the line. The independentvalue is theslopeof the
regression line; since the line is linear theslope isalsoconstant. Thesignificance is theactual ‘p’values.
StandardError ( √
n)=σ
whereσ refers to thestandarddeviation,nrefers to thesamplesize.
3.1.1. Population
As depicted in Table 1, the constant value is−591,193.3447. The independent value that is,
the slope of the regression line is 959,469.219; since the line is linear the slope is also constant.
Theregressionequationusually framesapredictionandtheprecisionof theprediction iscalculatedby
meansof thestandarderror. Italsomeasures thescatterordispersionof theobservedvaluesaround
theregression line.
Y=959,469.219X−591,193.347 is theregressionequation.
Table1.Summaryof themodelwithPopulationas thevariable.
Ind.Variable RSquare Std. Error Constant Slope Significance
Population 0.845 89,127.342 −591,193.347 959,469.219 0.000
3.1.2.GDP
Asillustrated inTable2, theconstantvalue is53,096.385. The independentvalue that is, theslope
of theregression line is417,965.826; since the line is linear theslope isalsoconstant.
Y=417,965.826X−53,096.385 is theregressionequation.
Table2.Summaryof themodelwithGDPas thevariable.
Ind.Variable RSquare Std. Error Constant Slope Significance
GDP 0.957 46,784.201 53,096.385 417,965.826 0.000
3.1.3.GDPperCapita
Theconstantvalue is−2457.344. The independentvalue that is, theslopeof theregression line is
959,469.511; since the line is linear theslope isalsoconstantas inTable3.
Y=546.511X−2457.344 is theregressionequation.
Table3.Summaryof themodelwithGDPpercapitaas thevariable.
Ind.Variable RSquare Std. Error Constant Slope Significance
GDP/Capita 0.951 50,234.297 −2457.344 546.511 0.000
WhenweforecastTotalElectricityConsumption(TEC)usingthreevariables, theGDPplaysan
important roleanditpredictsbetter theTotalElectricityConsumptionthantheGDPperCapitaand
thepopulation. TheR2value forGDPandTECis0.957whereasbetweenGDPpercapitaandTEC
it is only0.951. WhencomparedwithPopulationandTECit is evenas loweras0.845. Hence it is
concludedthatGDPforeseesTECbetter. The loweststd. error,46,784.201ofall the three isalsowith
theGDP.
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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