Optimal features for power usage prediction based on LightGBM

Abstract

This study introduces a method for forecasting electricity usage outliers employing the LightGBM algorithm. Previous research has demonstrated the capability of deep learning models to predict electricity usage trends; however, these models often exhibit low accuracy when forecasting outliers that cause electricity use to jump. Accurately predicting when electricity usage will reach outlier values several hours in advance is crucial for power companies to ensure appropriate electricity supply. We employed LightGBM, a machine learning technique, to predict outliers and successfully demonstrated its effectiveness in achieving reliable prediction accuracy. Additionally, we have successfully identified the key features essential for accurate outlier prediction.