Zhou Shiyang, Zhang Qingyong, Xiao Peng, Xu Bingrong, Luo Geshuai
School of Automation, Wuhan University of Technology, Wuhan, China.
Sci Rep. 2025 Feb 4;15(1):4282. doi: 10.1038/s41598-025-88566-4.
Accurate short-term load forecasting (STLF) provides important support for the economic and stable operation of the power system. Although various deep learning methods have achieved good results in STLF, they usually model load features only from a limited perspective, i.e., they do not uniformly utilize the three features of multivariate load data: the influence of covariates, multiscale features and local-global variations. The insufficient mining of these three features limits the improvement of prediction accuracy. To address the above problems, we design a novel STLF model called UniLF based on Transformer framework, which contains the proposed convolutional enhancement-fusion embedding method to capture the correlations between load and covariates for embedding, the proposed feature reconstruction-decomposition block to distill multiscale features as well as more detailed local-global variations from 2D space and the core mask-guided multiscale interactive self-attention mechanism to further realize the enhanced interactions of scale features and temporal features. Experiments conducted on three load datasets from Australia, Panama and Austria show that UniLF achieves superior forecasting accuracy with competitive practical efficiency under different prediction lengths, providing a new solution for STLF.
准确的短期负荷预测（STLF）为电力系统的经济稳定运行提供了重要支持。尽管各种深度学习方法在STLF中取得了良好的效果，但它们通常仅从有限的角度对负荷特征进行建模，即它们没有统一利用多变量负荷数据的三个特征：协变量的影响、多尺度特征和局部-全局变化。对这三个特征的挖掘不足限制了预测精度的提高。为了解决上述问题，我们基于Transformer框架设计了一种名为UniLF的新型STLF模型，该模型包含所提出的卷积增强-融合嵌入方法，用于捕获负荷与协变量之间的相关性以进行嵌入；所提出的特征重构-分解块，用于从二维空间中提取多尺度特征以及更详细的局部-全局变化；以及核心掩码引导的多尺度交互式自注意力机制，以进一步实现尺度特征和时间特征的增强交互。在来自澳大利亚、巴拿马和奥地利的三个负荷数据集上进行的实验表明，UniLF在不同预测长度下以具有竞争力的实际效率实现了卓越的预测精度，为STLF提供了一种新的解决方案。
