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

arXiv: https://arxiv.org/abs/2009.06527 · HTML: https://mlsanity.com/paper/2009.06527/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 23 of 23)

- [From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection](https://mlsanity.com/paper/2409.17515/) (2024)
- [Adaptive Probabilistic Forecasting of Electricity (Net-)Load](https://mlsanity.com/paper/2301.10090/) (2023)
- [DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks](https://mlsanity.com/paper/2305.08767/) (2023)
- [News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand](https://mlsanity.com/paper/2301.07535/) (2023)
- [In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance](https://mlsanity.com/paper/2302.13046/) (2023)
- [Hierarchical transfer learning with applications for electricity load forecasting](https://mlsanity.com/paper/2111.08512/) (2021)
- [State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting](https://mlsanity.com/paper/2110.00334/) (2021)
- [Efficient mid-term forecasting of hourly electricity load using generalized additive models](https://mlsanity.com/paper/2405.17070/) (2024)
- [Leveraging Graph Neural Networks to Forecast Electricity Consumption](https://mlsanity.com/paper/2408.17366/) (2024)
- [Forecasting time series with constraints](https://mlsanity.com/paper/2502.10485/) (2025)
- [Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models](https://mlsanity.com/paper/2302.08192/) (2023)
- [Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition](https://mlsanity.com/paper/2402.11664/) (2024)
- [Adaptive time series forecasting with markovian variance switching](https://mlsanity.com/paper/2402.14684/) (2024)
- [Multi-task Online Learning for Probabilistic Load Forecasting](https://mlsanity.com/paper/2502.04163/) (2025)
- [Graph Neural Networks for Electricity Load Forecasting](https://mlsanity.com/paper/2507.03690/) (2025)
- [Multi-Modal Time Series Prediction via Mixture of Modulated Experts](https://mlsanity.com/paper/2601.21547/) (2026)
- [News and Load: Social and Economic Drivers of Regional Multi-horizon Electricity Demand Forecasting](https://mlsanity.com/paper/2406.06641/) (2024)
- [AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting](https://mlsanity.com/paper/2503.24019/) (2025)
- [Adaptive Multi-task Learning for Probabilistic Load Forecasting](https://mlsanity.com/paper/2512.20232/) (2025)
- [Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation](https://mlsanity.com/paper/2605.06541/) (2026)
- [From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting](https://mlsanity.com/paper/2606.03097/) (2026)
- [Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting](https://mlsanity.com/paper/2606.23425/) (2026)
- [Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testing](https://mlsanity.com/paper/2610.02608/) (2026)

## Abstract (arXiv)

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.