A Comparative Study of Machine Learning Models for Short-Term Load Forecasting

A Comparative Study of Machine Learning Models for Short-Term Load Forecasting

Short-Term Load Forecasting (STLF) was a critical task in power system operations, enabling efficient energy management and planning. This study presented a comparative analysis of five machine learning models namely XGBoost, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SV...

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Bibliographic Details
Main Authors: Etna Vianita, Henri Tantyoko
Format: Article
Language:English
Published: Universitas Diponegoro 2025-05-01
Series:Jurnal Masyarakat Informatika
Subjects:
short-term load forecasting, machine learning models, lag features, electricity demand prediction, model evaluation
Online Access:https://ejournal.undip.ac.id/index.php/jmasif/article/view/73130
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