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 marketand 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 .
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 .
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even more difficult to obtain. The increasing power market integration has complicated the forecasting process, where
difficult for electricity market participants to obtain because
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considering features from both the local market and
the consideration of features from
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In this paper, we
This study provides a method of exploring the influence of market coupling on electricity price prediction. We
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deep learning models,
long short-term memory (LSTM) deep neural networks
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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
LSTM models have a good performance in handling nonlinear and complex problems and processing time series data
R
, we construct a broad set of features from the Nord Pool marketand its six coupling countries for forecasting the Nord Pool system price
of the Nordic market, the proposed models obtain considerably accurate results
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. 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
, 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