面向边缘计算的电力异步异构数据加速压缩

Hongkai Wang, Hanyu Rao, Xiaogang Gong, Zuge Chen, Dong Mao, Jingyao Zhang
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引用次数: 0

摘要

随着边缘计算和电力场景云计算的发展,云中心每天从边缘节点采集大量数据,导致边缘节点负载过载,传输时延增大,数据在云中心的存储和使用变得困难。节点的通信能力、存储能力和计算能力面临更大的挑战。负荷数据是电力异步异构数据中最重要的结构数据。为了减少边缘网络在传输过程中产生的数据量,可以利用压缩技术对负载数据进行有效压缩。在使用传统的集成神经网络模型对时间序列负荷数据进行压缩之前,需要计算窗口数据的方差,并将得到的方差与经验阈值进行比较,从而将负荷数据分为稳定数据和不稳定数据。由于数据预处理逻辑复杂,整体压缩计算耗时,并且由于需要手动设置经验参数,数据分类算法的鲁棒性不高。本文将多层感知器应用于负荷数据分类,结合集成神经网络模型,构建一种可应用于智能电网场景的边侧数据压缩方案。该方案在保证原始压缩比的基础上实现了更快的压缩速度。
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Electric Power Asynchronous Heterogeneous Data Accelerated Compression for Edge Computing
With the development of edge computing and cloud computing in power scenarios, the cloud center collects a large amount of data from edge nodes every day, and the load of edge nodes is overloaded and the transmission delay increases, making it difficult to store and use data in the cloud center. The communication capabilities, storage capabilities and computing capabilities of nodes face greater challenges. Load data is the most important structural data in the asynchronous heterogeneous data of electric power. In order to reduce the amount of data generated during the transmission process of the edge network, compression technology can be used to effectively compress the load data. Before using the traditional integrated neural network model to compress the time series load data, it is necessary to calculate the variance of the window data, and compare the obtained variance with the empirical threshold, so as to divide the load data into stable data and unstable data. Due to the complex logic of data preprocessing, the overall compression calculation is time-consuming, and the robustness of the data classification algorithm is not high due to the need to manually set empirical parameters. In this paper, the multilayer perceptron is applied to load data classification, combined with the integrated neural network model, to construct an edge-side data compression scheme that can be applied to smart grid scenarios. This scheme achieves faster compression speed on the basis of ensuring the original compression ratio.
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