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The Internet of Things adoption in the manufacturing industry allowsenterprises to monitor their electrical power consumption in real time and atmachine level. In this paper, we follow up on such emerging opportunities fordata acquisition and show that analyzing power consumption in manufacturingenterprises can serve a variety of purposes. Apart from the prevalent goal ofreducing overall power consumption for economical and ecological reasons, suchdata can, for example, be used to improve production processes.  Based on a literature review and expert interviews, we discuss how analyzingpower consumption data can serve the goals reporting, optimization, faultdetection, and predictive maintenance. To tackle these goals, we propose toimplement the measures real-time data processing, multi-level monitoring,temporal aggregation, correlation, anomaly detection, forecasting,visualization, and alerting in software.  We transfer our findings to two manufacturing enterprises and show how thepresented goals reflect in these enterprises. In a pilot implementation of apower consumption analytics platform, we show how our proposed measures can beimplemented with a microservice-based architecture, stream processingtechniques, and the fog computing paradigm. We provide the implementations asopen source as well as a public demo allowing to reproduce and extend ourresearch.

Goals and Measures for Analyzing Power Consumption Data in Manufacturing Enterprises