基于变量选择与Transformer模型的中长期电力负荷预测方法

Mid-long term power load forecasting based variable selection and transformer model

准确且有效的负荷预测对于电力系统的实时运行和调度非常重要。提出了一种融合变量选择与稀疏Transformer模型的预测方法,将静态变量和时序变量作为输入,充分发挥静态变量在全局时间范围内的信息增强作用,基于门控机制设计变量分权组件,根据变量与预测结果的相关性,赋予变量不同的权重。设计了双层编码结构,进行时序特征提取,对注意力进行稀疏处理,通过多变量输入对未来时刻负荷进行预测。基于真实电力负荷数据的实验表明,本文模型能够提高中长期负荷预测精度和效率。

Accurate and effective load forecasting is very important for real-time operation and dispatching of power systems.In this paper,a prediction model that incorporates variable selection and sparse Transformer is proposed.Static and temporal variables are used as inputs to give full play to the information enhancement of static variables in the global time range.The variable weighting component is designed based on the gating mechanism with which different weights are assigned to the variables according to their relevance to the predicted output.A two-layer coding structure is designed for temporal feature extraction,attention is sparse,and future moment loads are predicted by multivariate inputs.The proposed model is validated using real power load data,and the experimental results show that it can improve the prediction accuracy and prediction efficiency of mid-long term load forecasting.