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• W4306149771 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." @default.
• W4306149771 created "2022-10-14" @default.
• W4306149771 creator A5038525019 @default.
• W4306149771 date "2022-10-13" @default.
• W4306149771 modified "2023-10-05" @default.
• W4306149771 title "A Comprehensive Study of Random Forest for Short-Term Load Forecasting" @default.
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• W4306149771 doi "https://doi.org/10.3390/en15207547" @default.
• W4306149771 hasPublicationYear "2022" @default.
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