Prediction of Energy Storage Frequency Regulation Commands Based on Multi-scale Entropy Dynamic Alignment Fusion Method Combined with AFGRU

LI Zhipeng; JIANG Nan; SUN Guohui; XIE Jia; CUI Lei

doi:10.14044/j.1674-1757.pcrpc.2026.02.004

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Prediction of Energy Storage Frequency Regulation Commands Based on Multi-scale Entropy Dynamic Alignment Fusion Method Combined with AFGRU

POWER CAPACITOR & REACTIVE POWER COMPENSATION | Development of New Energy Storage Technologies Such as Supercapacitors and Their Applications in the New Power System

Prediction of Energy Storage Frequency Regulation Commands Based on Multi-scale Entropy Dynamic Alignment Fusion Method Combined with AFGRU
基于多尺度熵-动态对齐融合法结合AFGRU的储能调频指令预测
Xi'an Thermal Power Research Institute Co., Ltd., Xi'an 710048, China
Huaneng Yimin Coal and Electricity Co., Ltd., Inner Mongolia Hulunbuir 021000, China
Xi'an University of Petroleum, Xi'an 710065, China
Huaneng Rizhao Power Plant, Shandong Rizhao 276800, China
Vol. 47, Issue 2, Pages: 37-44(2026)
DOI：10.14044/j.1674-1757.pcrpc.2026.02.004
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LI Zhipeng, JIANG Nan, SUN Guohui, et al. Prediction of Energy Storage Frequency Regulation Commands Based on Multi-scale Entropy Dynamic Alignment Fusion Method Combined with AFGRU[J]. Power Capacitor & Reactive Power Compensation, 2026, 47(2): 37-44.
DOI：

LI Zhipeng, JIANG Nan, SUN Guohui, et al. Prediction of Energy Storage Frequency Regulation Commands Based on Multi-scale Entropy Dynamic Alignment Fusion Method Combined with AFGRU[J]. Power Capacitor & Reactive Power Compensation, 2026, 47(2): 37-44. DOI： 10.14044/j.1674-1757.pcrpc.2026.02.004.

摘要

为提升电厂调频指令序列的预测精度，提出一种融合多尺度熵-动态对齐融合法（multi scale entropy dynamic alignment fusion，MSEDAF）与改进神经网络的组合预测模型。该方法首先将原始序列经VMD分解为6个子序列（intrinsic mode function，IMF），进而采用创新的多尺度熵-动态对齐融合法（multi scale entropy dynamic alignment fusion，MSEDAF）通过结合多尺度熵度量与动态时间规整的复合相似性计算，将规律性相似的子序列两两融合为3个综合分量（Fused）；随后，设计自适应特征门控单元（adaptive featuregated recurrent unit，AFGRU）网络，其核心特点在于注意力机制和特征增强模块，对3个融合子序列并行预测并重构最终结果。基于内蒙古某电厂调频数据的实验表明：MSEDAF-VMD-AFGRU预测的MAE较标准GRU平均降低90.14%。该组合模型通过物理规律引导的特征融合与自适应门控机制，显著增强了复杂调频场景的预测精确度，为电网AGC控制系统提供了高精度支撑。

Abstract

For improving the prediction accuracy of frequency regulation command sequences in power plants，a combined prediction model integrating multi cale entropy dynamic alignment fusion（MSEDAF） and improved neural networks is proposed. This method firstly decomposes the original sequence into 6 subsequences（Intrinsic Mode Function，IMF）through VMD，and then uses the innovative Multi scale entropy dynamic alignment fusion method（MSEDAF）to combine multi-scale entropy measurement with dynamic time regularization composite similarity calculation，and fuse the subsequences with similar regularity into 3 comprehensive components（Fused）pairwise.Then，an adaptive feature gated recurrent unit （AFGRU）network is designed，with its core features being attention mechanism and feature enhancement module，to predict and reconstruct the final results in parallel for the three fused subsequences. The experiments based on frequency regulation data from a power plant in Inner Mongolia show that the MAE predicted byMSEDAF-VMD-AFGRUis reduced by an average of 90.14% compared to the standard GRU. This combination model significantly enhances the prediction accuracy of complex frequency modulation scenarios through feature fusion guided by physical laws and adaptive gating mechanism，providing high precision support for the AGC control system of power grid.

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