  We focus on day-ahead electricity load forecasting of substations of the
distribution network in France; therefore, our problem lies between the
instability of a single consumption and the stability of a countrywide total
demand. Moreover, we are interested in forecasting the loads of over one
thousand substations; consequently, we are in the context of forecasting
multiple time series. To that end, we rely on an adaptive methodology that
provided excellent results at a national scale; the idea is to combine
generalized additive models with state-space representations. However, the
extension of this methodology to the prediction of over a thousand time series
raises a computational issue. We solve it by developing a frugal variant,
reducing the number of parameters estimated; we estimate the forecasting models
only for a few time series and achieve transfer learning by relying on
aggregation of experts. It yields a reduction of computational needs and their
associated emissions. We build several variants, corresponding to different
levels of parameter transfer, and we look for the best trade-off between
accuracy and frugality. The selected method achieves competitive results
compared to state-of-the-art individual models. Finally, we highlight the
interpretability of the models, which is important for operational
applications.

Machine Learning;Applications;Methodology;Machine Learning