Technical Papers Parallel Session-IV: Modelling locational marginal prices using decision tree
Abstract/Description
In this work, Decision Tree are utilized to model and predict power system Locational Marginal Prices (LMP). We determine key power system variables that affect LMP and these are the input attributes fed to the decision tree with the output attribute as numeric LMP values. The decision tree algorithm investigated is the Random Forest Decision Tree and a comparison is made with a linear regression model. Results show that DT can be efficiently utilized in LMP prediction with high reliability and minimal errors.
Keywords
Location
Theatre 1, Aman Tower
Session Theme
Technical Papers Parallel Session-IV: Artificial Intelligence
Session Type
Parallel Technical Session
Session Chair
Dr. Syeda Saleha Raza
Start Date
31-12-2017 3:40 PM
End Date
31-12-2017 4:00 PM
Recommended Citation
Nwulu, N. I. (2017). Technical Papers Parallel Session-IV: Modelling locational marginal prices using decision tree. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2017/2017/27
COinS
Technical Papers Parallel Session-IV: Modelling locational marginal prices using decision tree
Theatre 1, Aman Tower
In this work, Decision Tree are utilized to model and predict power system Locational Marginal Prices (LMP). We determine key power system variables that affect LMP and these are the input attributes fed to the decision tree with the output attribute as numeric LMP values. The decision tree algorithm investigated is the Random Forest Decision Tree and a comparison is made with a linear regression model. Results show that DT can be efficiently utilized in LMP prediction with high reliability and minimal errors.
