BRIM: An accurate electricity spot price prediction scheme-based bidirectional recurrent neural network and integrated market

Yiyuan Chen, Yufeng Wang, Jianhua Ma, Qun Jin

研究成果: Article

抄録

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.

元の言語English
記事番号2241
ジャーナルEnergies
12
発行部数11
DOI
出版物ステータスPublished - 2019 6 12

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Recurrent neural networks
Recurrent Neural Networks
Electricity
Prediction
Memory Term
Forecasting
Forecast
Power System Stability
Electricity Market
Processing
System stability
Neurons
Time Series Data
Market
Time series
Profitability
Volatility
Profit
Neuron
Benchmark

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

これを引用

BRIM : An accurate electricity spot price prediction scheme-based bidirectional recurrent neural network and integrated market. / Chen, Yiyuan; Wang, Yufeng; Ma, Jianhua; Jin, Qun.

:: Energies, 巻 12, 番号 11, 2241, 12.06.2019.

研究成果: Article

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