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Outlier-adaptive-based non-crossing quantiles method for day-ahead electricity price forecasting

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  • Chen, Zhengganzhe
  • Zhang, Bin
  • Du, Chenglong
  • Yang, Chunhua
  • Gui, Weihua

Abstract

In deregulated electricity markets, accurate and reliable day-ahead electricity price forecasting (EPF) is beneficial for hedging volatility risks, implementing dispatch controls, and formulating bidding strategies. As the electricity price series is characterized by high volatility, non-stationarity and multi-seasonality, predicting its future trend poses a challenge. Moreover, most existing forecasting models lack tolerance for outliers disturbance, which is insufficient to cover the demands of actual wholesale market. To this end, an outlier-adaptive-based non-crossing quantiles regression (OANQR) model is proposed for day-ahead EPF. In this model, to tackle disturbances from price outliers, local outlier factor (LOF) module is implemented for detecting and isolating abnormal price. Then, the filtered non-stationary price series is decomposed into sub-sequence using variational mode decomposition (VMD) module, reducing complexity and randomness. In terms of the temporal variability, a non-crossing quantiles regression is proposed by integrating cross-conformal symmetry and skip connection recurrent neural network to enhance the reliability and sharpness of the forecasted electricity price intervals. Finally, through validation on real data from different electricity markets, the study reveals that the developed OANQR surpasses existing models with respect to deterministic and interval forecasting of day-ahead EPF.

Suggested Citation

  • Chen, Zhengganzhe & Zhang, Bin & Du, Chenglong & Yang, Chunhua & Gui, Weihua, 2025. "Outlier-adaptive-based non-crossing quantiles method for day-ahead electricity price forecasting," Applied Energy, Elsevier, vol. 382(C).
    • Handle: RePEc:eee:appene:v:382:y:2025:i:c:s0306261925000583
    DOI: 10.1016/j.apenergy.2025.125328
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    References listed on IDEAS

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    1. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    2. Monjazeb, Mohammad Reza & Amiri, Hossein & Movahedi, Akram, 2024. "Wholesale electricity price forecasting by Quantile Regression and Kalman Filter method," Energy, Elsevier, vol. 290(C).
    3. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    4. Loizidis, Stylianos & Kyprianou, Andreas & Georghiou, George E., 2024. "Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets," Applied Energy, Elsevier, vol. 363(C).
    5. Liu, Xin & Yang, Luoxiao & Zhang, Zijun, 2022. "The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions," Applied Energy, Elsevier, vol. 324(C).
    6. He, Miao & Jiang, Weiwei & Gu, Weixi, 2024. "TriChronoNet: Advancing electricity price prediction with Multi-module fusion," Applied Energy, Elsevier, vol. 371(C).
    7. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
    8. Meng, Anbo & Zhu, Jianbin & Yan, Baiping & Yin, Hao, 2024. "Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network," Applied Energy, Elsevier, vol. 369(C).
    9. Sai, Wei & Pan, Zehua & Liu, Siyu & Jiao, Zhenjun & Zhong, Zheng & Miao, Bin & Chan, Siew Hwa, 2023. "Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms," Applied Energy, Elsevier, vol. 352(C).
    10. Wu, Han & Liang, Yan & Heng, Jia-Ni & Ma, Chao-Xiong & Gao, Xiao-Zhi, 2024. "MSV-net: Multi-scale visual-inspired network for short-term electricity price forecasting," Energy, Elsevier, vol. 291(C).
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