  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 even more difficult to obtain. The increasing power market integration
has complicated the forecasting process, where electricity forecasting requires
considering features from both the local market and ever-growing coupling
markets. In this paper, we apply state-of-the-art deep learning models,
combined with feature selection algorithms for electricity price prediction
under the consideration of market coupling. We propose three hybrid
architectures of long-short term memory (LSTM) deep neural networks and compare
the prediction performance, in terms of various feature selections. In our
empirical study, we construct a broad set of features from the Nord Pool market
and its six coupling countries for forecasting the Nord Pool system price. The
results show that feature selection is essential to achieving accurate
prediction. Superior feature selection algorithms filter meaningful
information, eliminate irrelevant information, and further improve the
forecasting accuracy of LSTM-based deep neural networks. The proposed models
obtain considerably accurate results.
