Hierarchical transfer learning with applications to electricity load forecasting

The recent abundance of electricity consumption data available at different scales provides new opportunities and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales. In this study, we take advantage of the similarity between this hierarchical prediction problem and transfer learning where source data are observed at a low aggregation level and target data at a global level. We develop two methods for hierarchical transfer learning based on stacking generalized additive models and random forests (GAM-RF). We also propose and compare adaptations of online aggregation of experts in a hierarchical context using quantile GAM-RF as experts. We apply these methods to two electricity load forecasting problems at the national scale by using smart meter data in the first case and regional data in the second case. For these two user cases, we compared the performance of our methods and benchmark algorithms, and investigated their behavior using variable importance analysis. Our results demonstrate that both methods can lead to significantly improved predictions.