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Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting

Published: August 25, 2025 | arXiv ID: 2508.17700v1

By: Junying Yang , Gang Lu , Xiaoqing Yan and more

Potential Business Impact:

Fills in missing data to predict future needs.

Business Areas:
Predictive Analytics Artificial Intelligence, Data and Analytics, Software

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.

Page Count
5 pages

Category
Computer Science:
Machine Learning (CS)