Machine learning based analysis of factory energy load curves with focus on transition times for anomaly detection

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.