基于CEEMDAN-CNN-LSTM的供热异常数据检测与清洗
Heating data detection and cleaning based on CEEMDAN-CNN-LSTM
梁晓龙 1李金刚 1徐平平 1马雅楠 2孟现阳2
作者信息
- 1. 国能宁夏供热有限公司 银川 750004
- 2. 西安交通大学热流科学与工程教育部重点实验室 西安 710049
- 折叠
摘要
利用供热系统的准确参数,对监测系统状态、识别异常情况具有指导意义.然而大量终端数据,可能存在失真问题,为此本文提出了一种异常数据检测和清洗方法.采用信号模态分解结合深度学习,构建数据检测与清洗模型.首先对由DeST获取的供热负荷进行CEEMDAN模态分解;其次将产生的本征模函数和剩余量输入CNN-LSTM深度学习预测模型,获取高精度结果;最后基于预测值和待清洗数据的偏差,完成检测和数据清洗,提高末端数据准确性.结果表明:本文的CEEMDAN-CNN-LSTM组合模型,识别异常数据的准确率和F1分数达到:91.36％,86.21％,优于其他3种模型;利用预测值替换异常值,保证数据集的完整准确.
Abstract
Using the accurate parameters of the heating system has guiding significance for monitoring system status and identifying abnormal conditions.However,a large amount of terminal data may have distortion problems.To address this,this paper proposed a method for detecting and cleaning abnormal data.Signal modal decomposition combined with deep learning was used to construct a detection and cleaning model.The first step involves conducting CEEMDAN mode decomposition of the heating load obtained by DeST.Subsequently,the intrinsic mode functions and residual quantities generated from the decomposition are input into the CNN-LSTM deep learning prediction model to achieve high-precision prediction results.Finally,based on the deviation between predicted values and data to be cleaned,abnormal detection and data cleaning are completed.The CEEEMDAN-CNN-LSTM combined model in this paper achieves superior accuracy and F1 scores of 91.36％ and 86.21％,respectively,outperforming the other three models.Moreover,the predicted values can be used to replace abnormal values,ensuring the integrity and accuracy of the final data set.
关键词
异常检测和清洗/模态分解/深度学习/供热系统Key words
anomaly detection and cleaning/modal decomposition/deep learning/heating system引用本文复制引用
基金项目
陕西省自然科学基础研究计划(2024JC-YBMS-257)
出版年
2024
NETL
NSTL
万方数据