mlsanityData to 14 Oct 2026

Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting

arXiv 2508.17700 ↗First posted 25 Aug 2025cs.LGcited by 0

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Abstract (arXiv)

Machine learning (ML) is capable of accurate Load Forecasting from complete data. However, there are many uncertainties that affect data collection, leading to sparsity. This article proposed a model called Adaptive Ensemble Learning with Gaussian Copula to deal with sparsity, which contains three modules: data complementation, ML construction, and adaptive ensemble. First, it applies Gaussian Copula to eliminate sparsity. Then, we utilise five ML models to make predictions individually. Finally, it employs adaptive ensemble to get final weighted-sum result. Experiments have demonstrated that our model are robust.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 44 references that are in the map. First posted on arXiv 25 Aug 2025.

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References · 1

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166

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