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BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

Author

Listed:
  • Yiyuan Chen

    (College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210000, China)

  • Yufeng Wang

    (College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210000, China)

  • Jianhua Ma

    (Faculty of Computer & Information Sciences, Hosei University, Tokyo 184-8584, Japan)

  • Qun Jin

    (Department of Human Informatics and Cognitive Sciences, Waseda University, Saitama 359-1192, Japan)

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.

Suggested Citation

  • Yiyuan Chen & Yufeng Wang & Jianhua Ma & Qun Jin, 2019. "BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market," Energies, MDPI, vol. 12(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2241-:d:239123
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    References listed on IDEAS

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    10. Eric Cebekhulu & Adeiza James Onumanyi & Sherrin John Isaac, 2022. "Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids," Sustainability, MDPI, vol. 14(5), pages 1-26, February.
    11. Lehna, Malte & Scheller, Fabian & Herwartz, Helmut, 2022. "Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account," Energy Economics, Elsevier, vol. 106(C).
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