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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Diponegoro
2025-05-01
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| Series: | Jurnal Masyarakat Informatika |
| Subjects: | |
| Online Access: | https://ejournal.undip.ac.id/index.php/jmasif/article/view/73130 |
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