Honorine Royer’s scientific contributions

We propose statistical methods combining the Bayesian approach and deep learning for forecasting individual electrical consumption. This work is done in partnership with EDF. Two types of methodologies are developed: one relying on Bayesian neural networks, the other using deep learning for dimensionality reduction prior to clustering. Bayesian (non deep) models are then applied to the clusters. Firstly, we present a methodology to estimate a multi target regression model in high dimension with neural networks. It is applied to the prediction of individual load curves of non residential customers. Secondly, we present a Bayesian transfer learning approach adapted to panel data. The methodology is applied to forecasting the individual end-of-month consumption of residential customers, with short historical data, for specific clusters of customers. Those clusters are built using neural networks.