DOI: https://doi.org/10.14710/jmasif.16.1.73130

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

Department of Informatics, Universitas Diponegoro, Jl. Prof. Jacub Rais, Tembalang, Semarang, Indonesia 50275, Indonesia

Received: 11 May 2025; Revised: 26 May 2025; Accepted: 29 May 2025; Available online: 29 May 2025; Published: 31 May 2025.
Editor(s): Ferda Ernawan
Open Access Copyright (c) 2025 The authors. Published by Department of Informatics Universitas, Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

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 (SVR), and LightGBM using real-world electricity demand data collected over a four-month period. Two modeling approaches were explored: one using only time-based features (hour, day of the week, month), and another incorporating historical lag features (lag_1, lag_2, lag_3) to capture temporal patterns. The results showed that MLP with lag features achieved the best performance (RMSE: 57.63, MAE: 34.54, MAPE: 0.22), highlighting its ability to model nonlinear and sequential dependencies. In contrast, SVR and LightGBM experienced performance degradation when lag features were added, suggesting sensitivity to feature dimensionality and data volume. These findings emphasized the importance of model-feature alignment and temporal context in improving forecasting accuracy. Future work could explore the integration of external variables such as weather and holidays, as well as the application of advanced deep learning architectures like LSTM or hybrid models to further enhance robustness and generalizability.

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Keywords: Short-Term Load Forecasting, Machine Learning Models, Lag Features, Electricity Demand Prediction, Model Evaluation

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Section: Research Article
Language : EN
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