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.

翻译：本文聚焦于法国配电网变电站的日前电力负荷预测问题，该问题介于单点负荷的不稳定性与全国总需求的稳定性之间。我们需对超过一千座变电站的负荷进行预测，属于多元时间序列预测范畴。为此，采用一种在全国范围取得优异效果的自适应方法——将广义加性模型与状态空间表示相结合。然而，将此方法扩展到上千条时间序列预测时产生了计算难题。我们通过开发节约型变体解决此问题：仅对部分时间序列估计预测模型，并通过专家聚合实现迁移学习，从而显著降低计算需求及相应碳排放。我们构建了多个参数迁移程度各异的变体，以探寻精确性与节约性之间的最佳平衡。所选方法在精度上可与经典单模型相媲美。最后，我们强调模型的可解释性——这对实际运营至关重要。