摘要
文章主要研究深度学习在电力通信数据分析和预测中的应用,首先介绍深度学习的基础结构(如神经网络)及其在处理大数据和复杂数据集方面的优势,接着分析电力通信数据的大规模性和复杂性,并探讨数据预处理方法。研究聚焦于使用深度学习模型,尤其是长短期记忆网络和深度前馈神经网络,来提高电力系统性能指标的预测准确性。实验结果表明,优化的模型在电力负荷预测和设备状态监测方面显示出高准确率,证明了深度学习在电力系统数据分析领域的有效性,并为电力行业的智能化管理和决策提供了技术支持。
This study explores the application of deep learning in the analysis and prediction of power communication data.Initially,it introduces the foundational structures of deep learning,including neural networks,emphasizing their advantages in handling large and complex datasets.The article then analyzes the massive scale and complexity of power communication data and discusses methods for data preprocessing.The focus of the research is on utilizing deep learning models,particularly Long Short-Term Memory networks and Deep Feedforward Neural Networks,to enhance the accuracy of predictions for power system performance indicators.The experimental results demonstrate that the optimized models achieve high accuracy in predicting power load and monitoring equipment status.This paper validates the effectiveness of deep learning in the field of power system data analysis and provides technical support for the future intelligent management and decision-making in the power industry.
作者
付晖
王艳飞
FU Hui;WANG Yanfei(Key Laboratory of Geotechnical of Changjiang Science,Wuhan Hubei 430010,China;Hubei Electric Power Planning Design and Research Institute Co.,Ltd.,Wuhan Hubei 430040,China)
出处
《信息与电脑》
2023年第23期184-186,共3页
Information & Computer
关键词
深度学习
电力通信数据分析
预测模型
deep learning
power communication data analysis
prediction model
