Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Day-ahead electricity price prediction applying hybrid models of
  LSTM-based deep learning methods and feature selection algorithms under
  consideration of market coupling   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.

6
The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an