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Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learning

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  • 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) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.

Suggested Citation

  • Ziyang Wang & Masahiro Mae & Takeshi Yamane & Masato Ajisaka & Tatsuya Nakata & Ryuji Matsuhashi, 2024. "Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learni," Energies, MDPI, vol. 17(19), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4885-:d:1488606
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    References listed on IDEAS

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    1. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    2. Weitemeyer, Stefan & Kleinhans, David & Vogt, Thomas & Agert, Carsten, 2015. "Integration of Renewable Energy Sources in future power systems: The role of storage," Renewable Energy, Elsevier, vol. 75(C), pages 14-20.
    3. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    4. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    5. He, Kaijian & Xu, Yang & Zou, Yingchao & Tang, Ling, 2015. "Electricity price forecasts using a Curvelet denoising based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 1-9.
    6. Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
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