Year: 2025
Type: article
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.
DOI: 10.1587/nolta.16.401
Authors: Kyogo Yamanada, Yuya Matsuda, Jousuke Kuroiwa
Institution: University of Fukui
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411796103