Abstract
For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.
Highlights
Liberalization and deregulation of electricity markets are the general trends of the global power system
In the environment of electric power trade marketization, accurate electricity price forecasting is significant to all stakeholders in the electricity market, for economic benefits are realized by electricity trading, and the acquisition of electricity price information in advance makes it possible to earn more profits in electricity exchanges
Unlike the existed deep learning-based electricity forecasting schemes that feed the historical and future electricity prices into two separate forward deep learning networks, and with intentionally taking into account the signaling effect of other integrated markets, we propose a Bidirectional recurrent neural networks (RNNs) (LSTM) and Integrated Market based forecasting model (BRIM) for day-ahead electricity prices, and comprehensively evaluate its performance with real datasets in European markets
Summary
Liberalization and deregulation of electricity markets are the general trends of the global power system. With the growing complexity of electricity markets, the dynamics of electricity prices exhibit volatility, including non-storable nature of electrical energy, the need to maintain constant balance between supply and demand, inelastic demand over short time period, oligopolistic generation side, uncertainties in both load and generation sides, etc. Energies 2019, 12, 2241 viewpoint of the power system, accurate electricity forecasting can increase system load rate, reduce system operation cost, and ensure the safety and stability of the power system. With accurate electricity price forecasting, can market participants make smart decisions to gain profit in a competitive and volatile environment, and power system stability can be improved
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