Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models
This addresses the computational challenge of scaling forecasting to multiple time series in electricity distribution, though it is incremental as it builds on existing methods.
The paper tackles day-ahead electricity load forecasting for over a thousand substations in France by developing a frugal variant of an adaptive methodology, achieving competitive results with reduced computational needs and emissions.
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.