Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting

TL;DR

This paper introduces an adaptive ensemble learning model using Gaussian Copula to effectively handle data sparsity in load forecasting, combining data complementation, multiple ML models, and adaptive weighting for improved robustness.

Contribution

The novel integration of Gaussian Copula with ensemble learning specifically addresses data sparsity issues in load forecasting tasks.

Findings

Model demonstrates robustness in experiments

Effective handling of sparse data through Gaussian Copula

Improved load forecasting accuracy

Abstract

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.