Power marketing inspection model based on multi-branch residual attention network

Hongbo Wu, Yiming Li, Linjuan Zhang, Chao Qiu, Qi Li
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Abstract

In recent years, the scale of smart grid is becoming larger and larger. Big data, multi-dimensional, high intelligence and strong reliability have become the significant characteristics of modern power grid. The traditional inspection and monitoring work method is difficult to adapt to the needs of the new form. The traditional inspection method has the problems of low efficiency, limited scope and insufficient depth. Intelligent methods represented by deep learning are becoming more and more effective tools to solve the above problems. This paper proposes a power marketing audit model based on multi branch residual attention network. Compared with the existing models, this model has high accuracy, wide range and strong operability.
基于多分支剩余关注网络的电力营销检测模型
近年来,智能电网的规模越来越大。大数据、多维度、高智能、强可靠性已成为现代电网的显著特征。传统的检验监测工作方法难以适应新形式的需要。传统的检测方法存在效率低、范围有限、深度不够等问题。以深度学习为代表的智能方法正日益成为解决上述问题的有效工具。提出了一种基于多分支剩余注意网络的电力营销审计模型。与现有模型相比,该模型具有精度高、范围广、可操作性强的特点。
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