边缘计算和深度学习的智能电网短期负荷预测研究

Research on Short-term Load Forecasting of Smart Grid Based on Edge Computing and Deep Learning

本研究以深度学习和边缘计算为基础,详细阐述了电力系统负荷预测的方法,并在此基础上分别建立了基于深度学习理论的电力短期负荷预测模型和基于边缘计算电力负荷预测模型,然后基于MATLAB平台分别对两种模型所选取的数据集进行仿真分析,并以N市某充电站为例,分析对比了两种模型对充电站电力负荷预测的准确性。研究结果表明,深度学习模型输出值与真实值相差较小,相关系数为0.99899,所建立的深度学习模型在数据特征挖掘和分类方面有绝对优势。

This study based on the deep learning and edge calculation,expounds on the power system load forecasting methods,and on this basis,respectively,set up power short-term load forecasting model based on the theory of the deep learning and power load forecasting model based on edge of computing,and then the selected two models respectively based on MATLAB platform by simulating the data set.Taking a charging station in N city as an example,the accuracy of the two models for power load prediction of the charging station is analyzed and compared.The research results show that the output value of the deep learning model has a small difference with the real value,and the correlation coefficient is 0.99899.The established deep learning model has absolute advantages in data feature mining and classification.