论文标题
法国Covid-19期间的短期电力负载预测的自适应方法
Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France
论文作者
论文摘要
2019年冠状病毒病（COVID-19）大流行已敦促世界上许多政府严格封锁，如果所有非必需的企业都关闭，公民被命令留在家中。该政策的后果之一是电力消耗模式发生了重大变化。由于负载预测模型依赖于日历或气象信息并接受了历史数据的培训，因此它们无法捕获锁定造成的重大突破，并且自大流行开始以来表现出色。这使得电力生产的调度具有挑战性，并且对电力生产商和电网运营商都有很高的成本。在本文中，我们使用Kalman过滤器和微调来介绍自适应通用添加剂模型，以适应新的电力消耗模式。此外，从意大利封锁中的知识也被转移，以预测法国的行为变化。提出的方法用于预测法国锁定期间的电力需求，在那里它们证明了与传统模型相比，它们显着减少预测错误的能力。最终，专家聚合用于利用每个预测的特殊性，并进一步增强结果。
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.
