  The recent abundance of data on electricity consumption at different scales
opens new challenges and highlights the need for new techniques to leverage
information present at finer scales in order to improve forecasts at wider
scales. In this work, we take advantage of the similarity between this
hierarchical prediction problem and multi-scale transfer learning. We develop
two methods for hierarchical transfer learning, based respectively on the
stacking of generalized additive models and random forests, and on the use of
aggregation of experts. We apply these methods to two problems of electricity
load forecasting at national scale, using smart meter data in the first case,
and regional data in the second case. For these two usecases, we compare the
performances of our methods to that of benchmark algorithms, and we investigate
their behaviour using variable importance analysis. Our results demonstrate the
interest of both methods, which lead to a significant improvement of the
predictions.
