基于改进机器学习的电力系统运行状态预测研究
Research on Power System Operation State Prediction Based on Improved Machine Learning
费延波 1李贵林1
作者信息
- 1. 山东省济南热电集团有限公司,山东 济南 250000
- 折叠
摘要
电力系统的数据量庞大且维度高,如何从海量的数据中提取有效的特征信息具有重要意义,为此,提出了一种新的基于改进机器学习的电力系统运行状态预测方法.通过系统启动与登录、设备连接配置、数据采集、数据传输与接收等步骤采集电力系统运行数据,对采集的电力系统运行数据进行清洗、转换、存储以及特征挖掘处理,将挖掘的特征输入至电力系统运行状态预测模型中,得到电力系统运行状态预测结果.结果表明,基于改进机器学习的电力系统运行状态预测方法的预测准确率较高,具有较高的实际应用价值.
Abstract
The data volume and dimensionality of the power system are huge,and it is of great significance to extract effective feature information from the massive data.Therefore,a new power system operation state prediction method based on improved machine learning is proposed.Collect power system operation data through system startup and login,device connection configuration,data collection,data transmission and reception,etc.Clean,convert,store,and mine the collected power system operation data.Input the mined features into the power system operation status prediction model to obtain the power system operation status prediction results.The results indicate that the prediction accuracy of the power system operation state prediction method based on improved machine learning is high,and it has high practical application value.
关键词
改进机器学习/电力系统/运行状态/状态预测Key words
improving machine learning/power system/operating status/state prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据