Page - 105 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Image of the Page - 105 -
Text of the Page - 105 -
Energies2018,11, 3442
Figure4.Category-wiseElectricityConsumptionacrossvariouscountries (2015).
Figure5.Category-wise%Shares inElectricityConsumptionacrossvariouscountries (2015).
3.Results
3.1. LinearRegression
In order topredict the influencingvariable onTotal ElectricityConsumption (TEC) for India,
Linear Regression is used initially. GDP, Population andGDPperCapita are taken as the input
variableswhichareusedonebyone in linearregressiontopredictTEC.
Thedistancebetweentheactualvalueandthemeaniscalculatedandalso thedistancebetween
theestimated lineanditsmeaniscalculated in theregression line. Thecomparisonbetweenthe two
105
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