基于聚合自适应模型的多地区电力负荷日前节俭预测
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models
摘要：
我们专注于日前的电力负荷预测的变电站的配电网在法国，因此，我们的问题在于一个单一的消费和稳定的全国总需求之间的不稳定性。此外，我们有兴趣预测一千多个变电站的负荷，因此，我们是在预测多个时间序列的背景下。为此，我们依赖于一种自适应方法，在全国范围内提供了出色的结果;这个想法是将广义加性模型与状态空间表示相结合。然而，这种方法的扩展到超过一千个时间序列的预测提出了一个计算问题。我们解决它通过开发一个节俭的变体，减少估计的参数的数量，我们估计的预测模型只有少数时间序列，并实现迁移学习依靠专家的聚合。它减少了计算需求及其相关排放。我们建立了几个变体，对应于不同级别的参数传递，我们寻找准确性和节俭之间的最佳权衡。与最先进的单个模型相比，所选方法实现了具有竞争力的结果。最后，我们强调了模型的可解释性，这对业务应用程序很重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
