Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (1): 169-178.doi: 10.6040/j.issn.1672-3961.0.2024.194

• Electrical Engineering • Previous Articles    

Ultra-short-term thermal power unit load forecasting based on D-Mamba model

WANG Xinjian1, JING Zhibin1, MENG Fancheng1, SHI Jianguo1, ZHANG Minhao1, ZHANG Yifan1, WANG Qinghua2*, ZHU Yankai2   

  1. WANG Xinjian1, JING Zhibin1, MENG Fancheng1, SHI Jianguo1, ZHANG Minhao1, ZHANG Yifan1, WANG Qinghua2*, ZHU Yankai2(1. Inner Mongolia Electric Power( Group )Co., Ltd., Inner Mongolia Electric Power Dispatching Control Branch, Hohhot 010010, Inner Mongolia, China;
    2. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • Published:2026-02-03

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Abstract: China progressively promoted the transition toward a new power system, with thermal power shifting from a base-load to a peak-load source. In this evolving power-generation context, thermal power units faced stricter challenges in unit tests and diverse response assessments. Ultra-short-term load forecasting for power units needed to account for the operating state of the unit to accurately assess its near-future load adjustment capability. Precise ultra-short-term load forecasting was critical for revealing a unit's dynamic performance indicators and assisting real-time operational adjustments. For units equipped with Auto-Generation Control(AGC), the AGC command served as a pivotal factor in forecasting power-generation loads. Hence, leveraging the Mamba model, this paper introduced a dynamic correction module centered on the AGC command, thereby ensuring precise ultra-short-term unit load forecasting. The model's performance was validated using actual operational data from a power unit, demonstrating its capability to achieve more accurate load forecasts.

Key words: thermal power unit, deep learning, load forecasting, state space models, auto generation control

CLC Number: 

  • TM621.6
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