Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models

TL;DR

This paper presents a computationally efficient, transfer learning-based approach for day-ahead electricity load forecasting across numerous substations, balancing accuracy, interpretability, and environmental impact.

Contribution

It introduces a frugal, adaptive aggregation method for multiple time series forecasting, reducing computational costs while maintaining competitive accuracy.

Findings

Achieves accurate load forecasts with fewer parameters.

Reduces computational needs and emissions.

Maintains interpretability for operational use.

Abstract

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.