ENBIS Webinar by Yannig Goude: "Adaptive additive models for short-term electricity load forecasting during COVID-19 lockdown"11 January 2021; 12:00 – 13:00
Adaptive additive models for short-term electricity load forecasting during COVID-19 lockdown
Yannig Goude, EDF Lab Paris & University Paris-Saclay, France
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 electricity production challenging and has a high cost for both electricity producers and grid operators.
In this talk, I will present the work done with David Obst and Joseph de Vilmarest at EDF R&D and presented in https://arxiv.org/abs/2009.06527 . We propose 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 prediction and enhance results even further.
Yannig Goude is a Senior Researcher at EDF Lab Paris and Associate Professor at Universite Paris-Saclay, France. He has previously been a research-engineer at EDF R&D since 2008. He obtained his Ph.D. in mathematics in 2007 at the University Paris-Sud 11 Orsay. His research interests are electricity load forecasting, more generally time series analysis and forecasting, semi-parametric models, expert aggregation, and individual sequences.