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A Comprehensive Study of Random Forest for Short-Term Load Forecasting

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  • Grzegorz Dudek

    (Department of Electrical Engineering, Częstochowa University of Technology, 42-200 Częstochowa, Poland)

Abstract

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts.

Suggested Citation

  • Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7547-:d:941080
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    Cited by:

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    2. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Mohamad Abou Houran & Varun Kumar & Dinesh Kumar & Saurabh Mani Tripathi & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan, 2023. "A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection," Energies, MDPI, vol. 16(2), pages 1-23, January.
    3. Marcin Blachnik & Sławomir Walkowiak & Adam Kula, 2023. "Large Scale, Mid Term Wind Farms Power Generation Prediction," Energies, MDPI, vol. 16(5), pages 1-15, March.
    4. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    5. Tamás Storcz & Géza Várady & István Kistelegdi & Zsolt Ercsey, 2023. "Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation," Energies, MDPI, vol. 16(16), pages 1-20, August.
    6. Monika Zimmermann & Florian Ziel, 2024. "Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting," Papers 2408.00507, arXiv.org.
    7. Changzhi Li & Dandan Liu & Mao Wang & Hanlin Wang & Shuai Xu, 2023. "Detection of Outliers in Time Series Power Data Based on Prediction Errors," Energies, MDPI, vol. 16(2), pages 1-19, January.
    8. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.

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