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Energies2018,11, 3442
values that is, thedifferencebetweentheactualandmeanandthedifferencebetweentheestimated
andthemeangives theR2value.
1âR2={SSR/SST}
SSRrefers to theresidual sumofsquaresandSSTrefers to the total sumofsquares.
The standarderrorof theestimate is thedistancebetween theestimatedand theactualvalue.
Theconstantvalue isactually the âyâ interceptof the line. The independentvalue is theslopeof the
regression line; since the line is linear theslope isalsoconstant. ThesigniïŹcance is theactual âpâvalues.
StandardError ( â
n)=Ï
whereÏ refers to thestandarddeviation,nrefers to thesamplesize.
3.1.1. Population
As depicted in Table 1, the constant value isâ591,193.3447. 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=959,469.219Xâ591,193.347 is theregressionequation.
Table1.Summaryof themodelwithPopulationas thevariable.
Ind.Variable RSquare Std. Error Constant Slope SigniïŹcance
Population 0.845 89,127.342 â591,193.347 959,469.219 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=417,965.826Xâ53,096.385 is theregressionequation.
Table2.Summaryof themodelwithGDPas thevariable.
Ind.Variable RSquare Std. Error Constant Slope SigniïŹcance
GDP 0.957 46,784.201 53,096.385 417,965.826 0.000
3.1.3.GDPperCapita
Theconstantvalue isâ2457.344. The independentvalue that is, theslopeof theregression line is
959,469.511; since the line is linear theslope isalsoconstantas inTable3.
Y=546.511Xâ2457.344 is theregressionequation.
Table3.Summaryof themodelwithGDPpercapitaas thevariable.
Ind.Variable RSquare Std. Error Constant Slope SigniïŹcance
GDP/Capita 0.951 50,234.297 â2457.344 546.511 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