Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting
This work addresses load forecasting challenges for energy systems in the presence of sparse data, representing an incremental improvement by combining existing techniques like Gaussian Copula and ensemble methods.
The paper tackles load forecasting under data sparsity by proposing an Adaptive Ensemble Learning with Gaussian Copula model, which uses Gaussian Copula for data complementation, multiple ML models for predictions, and adaptive ensemble for final results, achieving robust performance in experiments.
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.