情态动词
计算机科学
期限(时间)
电力负荷
电力系统
非线性系统
相关系数
功率(物理)
控制理论(社会学)
算法
人工智能
机器学习
物理
化学
控制(管理)
高分子化学
量子力学
作者
Yihao Tang,Huafeng Cai
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:11: 61958-61967
被引量:6
标识
DOI:10.1109/access.2023.3273596
摘要
For the characteristics of fluctuation, periodicity and nonlinearity of power load data, this paper proposes a short-term power load forecasting model based on VMD-Pyraformer-Adan. Firstly, the variational modal decomposition (VMD) algorithm is used to modally decompose the electric load data, the over-zero rate and Pearson correlation coefficient are introduced to divide the modal components to obtain the low-frequency, mid-frequency and high-frequency parts, and the reconstructed data are formed with the original load data respectively. Secondly, the reconstructed data are input to the Pyraformer prediction network containing pyramidal attention module (PAM) and coarse-scale construction module (CSCM). Then a new momentum optimizer Adan is used to optimize the parameters of the prediction network. The final output prediction results. The experimental results show that the proposed model in the paper exhibits higher prediction accuracy compared with other models.
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