Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France

TL;DR

This paper introduces adaptive generalized additive models with Kalman filters and transfer learning to improve short-term electricity load forecasting during COVID-19 lockdowns, significantly reducing prediction errors.

Contribution

It presents novel adaptive modeling techniques that incorporate transfer learning and expert aggregation to handle abrupt consumption pattern changes during lockdowns.

Findings

Models outperform traditional methods in forecasting accuracy.

Transfer learning from Italy improves French load predictions.

Adaptive models effectively capture lockdown-induced consumption shifts.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calendar or meteorological information and are trained on historical data, they fail to capture the significant break caused by the lockdown and have exhibited poor performances since the beginning of the pandemic. This makes the scheduling of the electricity production challenging, and has a high cost for both electricity producers and grid operators. In this paper we introduce adaptive generalized additive models using Kalman filters and fine-tuning to adjust to new electricity consumption patterns. Additionally, knowledge from the lockdown in Italy is transferred to anticipate the change of behavior in France. The proposed methods are applied to forecast the electricity demand during the French lockdown period, where they demonstrate their ability to significantly reduce prediction errors compared to traditional models. Finally expert aggregation is used to leverage the specificities of each predictions and enhance results even further.