@inproceedings{ce717dd8cfa247689e5cceb31671d98d,
title = "Modelling locational marginal prices using decision trees",
abstract = "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 = "Decision trees, locational marginal price, power system",
author = "Nwulu, {Nnamdi I.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Information and Communication Technologies, ICICT 2017 ; Conference date: 30-12-2017 Through 31-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICICT.2017.8320181",
language = "English",
series = "2017 International Conference on Information and Communication Technologies, ICICT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "156--159",
editor = "Tariq Mahmood and Imran Rauf and Shakeel Khoja and Sayeed Ghani",
booktitle = "2017 International Conference on Information and Communication Technologies, ICICT 2017",
address = "United States",
}