Approaches for monitoring the energy consumption with machine learning methods

In times of rising energy costs and increasing customer awareness of sustainable production methods, many manufacturers take measures to reduce their energy consumption. However, after the realization of such activities the energy demand often tends to increase again due to e.g. leaks, clogged filters, defect valves or suboptimal parameter settings. In order to prevent this, it is necessary to quickly identify such increases by continuously monitoring the energy consumption and counteracting accordingly. Currently, the monitoring is either performed manually or by setting static threshold values. The manual control can be time consuming for large amounts of sensor data. By setting static threshold values only a fraction of the inefficiencies are disclosed. Another option is to use anomaly detection methods from the area of machine learning, which compare the actual sensor values with the expected ones. In this paper an overview about existing anomaly detection methods, which can be applied for this purpose, is presented