网络弹性机器学习框架用于准确的个人负载预测和智能电网中的异常检测
M Tayseer1, M Talaat2,3, Amr A Zamel4,5
1Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, P.O. 44519, Zagazig, Egypt.
Scientific reports
|December 17, 2025
概括
本研究介绍了一个KMEANS-NN模型,用于精确预测智能电网中的电力负载. 网络弹性系统提高了预测准确度,并检测到网络攻击,大大减少了计算时间.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 智能电网的发展需要准确和安全的电力负载预测,以实现高效的能源管理.
- 个人负载预测 (ILF) 对智能电网可靠性和运营效率至关重要.
- 网络安全威胁,如虚假数据注入攻击,对智能电网完整性构成风险.
研究的目的:
- 提出一个可扩展和网络弹性方法来预测个人智能电表水平上的电力消耗.
- 通过机器学习提高个人负载预测 (ILF) 的准确性并减少计算复杂性.
- 实施一个异常检测系统 (ADS),用于识别智能电表数据中的网络攻击.
主要方法:
- 利用K-MEANS集群和神经网络 (KMEANS-NN) 来增强ILF.
- 作为异常检测方案 (ADS),采用基于主要组件分析的一类支持向量机 (PCA-OCSVM).
- 使用5个月的来自埃及的真实世界智能电表数据验证了集成模型,包括模拟的网络攻击.
主要成果:
- KMEANS-NN显著减少了高达40%的平均绝对调整百分比误差 (MAAPE),并将计算时间从几天减少到几分钟.
- 拟议的异常检测系统 (ADS) 在检测网络攻击方面实现了高准确度 (99.9%),灵敏度 (99.8%),精度 (99.9%),特异性 (99.9%) 和F1得分 (99.8%).
- 综合模型在各种基准模型 (ARIMA,CTREE,MLP,NNETAR) 中证明了预测准确度的提高.
结论:
- 集成的KMEANS-NN和PCA-OCSVM模型为智能电网中的电力负载预测提供了一个可扩展,准确和网络弹性解决方案.
- 该方法显著提高了预测性能,并提供了对网络攻击的强有力的保护.
- 拟议的系统显示出在大型智能电网环境中部署的强大潜力,提高了能源管理和可靠性.
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