  The availability of accurate day-ahead electricity price forecasts is pivotal
for electricity market participants. In the context of trade liberalisation and
market harmonisation in the European markets, accurate price forecasting
becomes difficult for electricity market participants to obtain because
electricity forecasting requires the consideration of features from
ever-growing coupling markets. This study provides a method of exploring the
influence of market coupling on electricity price prediction. We apply
state-of-the-art long short-term memory (LSTM) deep neural networks combined
with feature selection algorithms for electricity price prediction under the
consideration of market coupling. LSTM models have a good performance in
handling nonlinear and complex problems and processing time series data. In our
empirical study of the Nordic market, the proposed models obtain considerably
accurate results. The results show that feature selection is essential to
achieving accurate prediction, and features from integrated markets have an
impact on prediction. The feature importance analysis implies that the German
market has a salient role in the price generation of Nord Pool.
