Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

短期负荷预测(STLF)对电网的日常运营至关重要。然而，电力需求时间序列的非线性、非平稳性和随机性使得STLF成为一项具有挑战性的任务。本研究通过考虑一组表示欧洲国家次日电力需求的27个时间序列，研究了一种名为迁移学习(TL)的特殊STLF情况下的性能。我们使用一种流行且易于实施的神经网络(NN)模型，并进行聚类分析以识别系列之间的相似模式并辅助TL。在这个背景下，我们编译并比较了两种不同的TL方法，一种包括聚类步骤，一种不包括，并与传统的NN训练设置进行了比较。我们的结果表明，TL可以胜过传统方法，特别是当考虑到聚类技术时。

基于转移学习的日前负荷预测：欧洲国家电力需求时间序列的案例研究