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The recent abundance of data on electricity consumption at different scalesopens new challenges and highlights the need for new techniques to leverageinformation present at finer scales in order to improve forecasts at widerscales. In this work, we take advantage of the similarity between thishierarchical prediction problem and multi-scale transfer learning. We developtwo methods for hierarchical transfer learning, based respectively on thestacking of generalized additive models and random forests, and on the use ofaggregation of experts. We apply these methods to two problems of electricityload 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 theperformances of our methods to that of benchmark algorithms, and we investigatetheir behaviour using variable importance analysis. Our results demonstrate theinterest of both methods, which lead to a significant improvement of thepredictions.

Hierarchical transfer learning with applications for electricity load forecasting