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

• URL: http://arxiv.org/abs/2302.08192v1
• Date: Thu, 16 Feb 2023 10:17:19 GMT
• Title: Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models
• Authors: Guillaume Lambert (EDF R&D), Bachir Hamrouche (EDF R&D), Joseph de Vilmarest
• Abstract summary: We focus on day-ahead electricity load forecasting of substations of the distribution network in France. We develop a frugal variant, reducing the number of parameters estimated, to achieve transfer learning. We highlight the interpretability of the models, which is important for operational applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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