Power outage prediction by using logistic regression and decision tree - Alia Yasmin, Nor Saidi; Nor Azuana, Ramli; Noryanti, Muhammad; Lilik Jamilatul, Awalin (IOP Publishing, 2021)
The occurrence of the power outage caused inconvenience to the customers including the energy suppliers. There are various factors that can trigger the power outage such as lightning, weather or animal. In this paper, the power outage prediction has been performed by using the datasets provided which are lightning data and tripping report. The machine learning method was carried out to predict the power outage occurrence by using the Classification Learner App in MATLAB. Before performing the machine learning method, the data went through the data pre-processing to ensure the data is clean and the significant feature for prediction can be selected to run in the Classification Learner App. The results of this research have shown that Fine Tree is the most suitable model to be used for the prediction of power outage. The results have been compared by using the Area Under Curve (AUC) in Receiving Operating Characteristic (ROC). Logistic Regression and Coarse Tree shows the lowest value of AUC compared to other model and Fine Tree has the highest value of AUC.