Synergistic Optimization and Risk Control: Integrated Price Forecasting Models

Mingshen Xu; Tianxiang Hu; Boyi Zhang; Hanrui Zhang; Kexiao Wu; Teng Zhang; Xiaotian Li; Qianxve Mo; Xianliu Feng

doi:10.62177/jaet.v2i2.361

Authors

Mingshen Xu Department of Mathematics and Physics, North China Electric Power University
Tianxiang Hu Department of Mathematics and Physics, North China Electric Power University
Boyi Zhang Department of Environmental Science and Engineering, North China Electric Power University
Hanrui Zhang Department of Computer, North China Electric Power University
Kexiao Wu Department of Automation, North China Electric Power University
Teng Zhang Department of Mathematics and Physics, North China Electric Power University
Xiaotian Li Department of Mathematics and Physics, North China Electric Power University
Qianxve Mo Department of Electrical Engineering, North China Electric Power University
Xianliu Feng Department of Environmental Science and Engineering, North China Electric Power University

DOI:

https://doi.org/10.62177/jaet.v2i2.361

Keywords:

Variational Mode Decomposition, Improved Transformer, CNN-LSTM, INFO Optimization, Intraday Price Forecasting

Abstract

This paper presents a hybrid intraday electricity price forecasting model—Info-VMD-iTransformer-CNN-LSTM—tailored for high-dimensional, non-stationary price series. First, variational mode decomposition (VMD) adaptively separates price signals into intrinsic modes, mitigating mode mixing and noise. Next, an improved Transformer (iTransformer) with enhanced positional encoding captures long-range dependencies, while CNN layers extract local spatio-temporal features and LSTM units model sequential dynamics. Finally, the INFO algorithm automates hyperparameter optimization, ensuring both high accuracy and robustness. Empirical evaluations demonstrate that our approach consistently outperforms existing benchmarks under volatile market conditions, making it well suited for real-time forecasting in modern power systems.

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