Electricity Demand Forecasting by Multi-Task Learning
This work addresses forecasting challenges for energy distribution networks, but it is incremental as it applies existing multi-task learning techniques to a specific domain.
The paper tackled electricity demand forecasting across multiple network nodes using kernel-based multi-task learning, demonstrating that output kernel learning with multiplicative structures outperforms additive models on smart meter data.
We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity in multiple nodes of a distribution network. We show that recently developed output kernel learning techniques are particularly well suited to solve this problem, as they allow to flexibly model the complex seasonal effects that characterize electricity demand data, while learning and exploiting correlations between multiple demand profiles. We also demonstrate that kernels with a multiplicative structure yield superior predictive performance with respect to the widely adopted (generalized) additive models. Our study is based on residential and industrial smart meter data provided by the Irish Commission for Energy Regulation (CER).