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Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Author

Listed:
  • Ziyang Wang

    (Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan)

  • Masahiro Mae

    (Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan)

  • Takeshi Yamane

    (Department of Energy Systems Research and Development, KYOCERA Corporation, Yokohama 220-0012, Japan)

  • Masato Ajisaka

    (Department of Energy Systems Research and Development, KYOCERA Corporation, Yokohama 220-0012, Japan)

  • Tatsuya Nakata

    (Department of Energy Systems Research and Development, KYOCERA Corporation, Yokohama 220-0012, Japan)

  • Ryuji Matsuhashi

    (Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan)

Abstract

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model’s robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel “policy-versus-policy” strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF.

Suggested Citation

  • Ziyang Wang & Masahiro Mae & Takeshi Yamane & Masato Ajisaka & Tatsuya Nakata & Ryuji Matsuhashi, 2024. "Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration," Energies, MDPI, vol. 17(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2687-:d:1406905
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    References listed on IDEAS

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