Few-shot Residential Load Forecasting Boosted by Learning to Ensemble

Jinhao Liang, Chenbei Lu, Wenqian Jiang, and Chenye Wu

Accepted by the 7th IEEE Conference on Energy Internet and Energy System Integration (EI2)

November 2023


Probabilistic forecasting can characterize the uncertainties and the dynamic trends of the future residential load, while massive data are required for popular forecasting methods. In this study, we consider probabilistic load forecasting for residential users who are only willing to provide limited data samples due to privacy concerns. To address this challenge, we analyze the characteristics of residential load and employ clustering-based few-shot learning methods to augment the data. Meanwhile, we combine different models, known as model ensemble, to further improve the performance. Compared with conventional ensemble methods using the linear combination, we adopt learning to ensemble, which captures the strengths of various models by learning the optimal nonlinear combination to avoid performance loss. We demonstrate that the proposed method outperforms conventional rivals theoretically and empirically. This method also sheds light on how varying the number of provided data can accommodate different privacy concerns.

Jinhao Liang

M.Phil Student in The Chinese University of Hong Kong, Shenzhen

Jinhao Liang