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A decomposable multi-period mixing algorithm for long-term load forecasting
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AI
DOI：10.1016/j.ijepes.2025.110888.png)
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Abstract
En 中文
• A multi-period algorithm integrates multi-resolution features for load forecasting. • The method enables accurate long-term forecasting with limited data. • A residual-based probabilistic technique quantifies forecasting uncertainty. • PCA and correlation analysis are used to create a feature set, improving accuracy. • The model is validated on various datasets.
Keywords:
Long-term load forecasting
Point forecasting
Probabilistic forecasting
Load decomposition
Multilayer perceptron
Journal
I
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5
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1.1W
Citations:
3.1W
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Cited Papers
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APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
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