Electricity price forecasting is an essential task for all the deregulated
markets of the world. The accurate prediction of the day-ahead electricity
prices is an active research field and available data from various markets can
be used as an input for forecasting. A collection of models have been proposed
for this task, but the fundamental question on how to use the available big
data is often neglected. In this paper, we propose to use transfer learning as
a tool for utilizing information from other electricity price markets for
forecasting. We pre-train a bidirectional Gated Recurrent Units (BGRU) network
on source markets and finally do a fine-tuning for the target market. Moreover,
we test different ways to use the input data from various markets in the
models. Our experiments on five different day-ahead markets indicate that
transfer learning improves the performance of electricity price forecasting in
a statistically significant manner