Machine learning based analysis of factory energy load curves with focus on transition times for anomaly detection
Procedia CIRP(2020)
摘要
Abstract An accurate understanding of energy load curves is the key for effective management of factory energy systems and basis for several energy applications (e.g. forecasts, anomaly detection). While load curve analysis has been a research topic with practical significance in many areas, there is a lack of methods particularly to evaluate different temporal transitions between energy states. Consequently, related energy saving potentials on factory level remain undetected. Against this background, the paper presents a methodology combining unsupervised univariate clustering and multivariate prediction based methods. Within an automotive use case for anomaly detection in energy performance management, those methods are getting applied and validated with real factory data.
更多查看译文
关键词
Energy state,transition estimation,clustering,prediction based variance analysis
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要