基于数据挖掘的热能储存异常数据检测方法  

Data mining-based anomaly detection method for thermal energy storage

作　　者：卢鸣[1] LU Ming(School of Mechatronics and Information Engineering,Wuxi Vocational College of Arts and Crafts,Yixing 214200,Jiangsu,China)

机构地区：[1]无锡工艺职业技术学院机电与信息工程学院,江苏宜兴214200

出　　处：《储能科学与技术》2025年第2期831-833,共3页Energy Storage Science and Technology

基　　金：宜兴市科技项目(2021SF02)。

摘　　要：随着智能化技术的不断进步,数据挖掘在热能储存系统的异常检测中至关重要,成为提升能源管理和系统安全性的关键技术手段。文章首先分析了深度挖掘的海量数据中的潜在异常模式,增强热能储存系统的智能自适应与故障预警能力。本文介绍了基于监督学习、无监督学习、深度学习的几种异常检测方法,提出工业热能储存系统中的异常检测应用、大数据的热能储存异常检测与优化实践、云计算的热能储存异常监控与预警系统实践应用,旨在推动热能储存系统智能化、精确化,以及自适应化的全面发展,进一步提升能源系统的可靠性与安全性。With the continuous advancement of intelligent technologies,data mining plays a crucial role in anomaly detection for thermal energy storage systems,serving as a key technological means to enhance energy management and system security.This article first analyzes potential anomaly patterns within large datasets through in-depth mining,thereby improving the intelligent adaptive capabilities and early fault warning systems of thermal energy storage systems.It introduces several anomaly detection methods based on supervised learning,unsupervised learning,and deep learning techniques.Furthermore,it proposes specific applications of anomaly detection in industrial thermal energy storage systems,explores practices for anomaly detection and optimization using big data,and discusses practical applications of cloud computing-based monitoring and early warning systems for thermal energy storage anomalies.The aim is to promote the comprehensive development of intelligent,precise,and adaptive thermal energy storage systems,thereby enhancing the reliability and security of energy systems.

关 键 词：智能电网 自动化控制 储能技术 协同应用 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]