Transfer learning for day-ahead load forecasting: a case study on
  European national electricity demand time series
Alexandros-Menelaos Tzortzis Sotiris Pelekis Evangelos Spiliotis Spiros Mouzakitis John Psarras Dimitris Askounis
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.