Abstract
AbstractA prerequisite to identify energy efficiency potentials and to improve energy efficiency is the measurement and analysis of the energy demand. However, in industrial practice, approaches to identify energy efficiency measures of production machines are associated with high costs for metering equipment and time consuming analysis requiring expertise. Against this background, this paper describes a comprehensive and cost-efficient framework from acquisition to analysis of energy data to serve as a starting point to increase energy efficiency in manufacturing. For this purpose, an energy transparency and analysis system is being developed that can measure, record and analyze electrical quantities. The validity of the data acquisition can be verified by utilizing a Raspberry Pi as a low-cost edge analyzer device. Measurement data is stored with associated metadata in a SQLite database for subsequent processing in a Python-based web application, in which machine learning algorithms can be deployed. The algorithms can be used to process vast amounts of data and to provide a basis for calculating energy performance indicators to reveal energy efficiency potentials. The overall workflow is validated using a lathe and a cleaning machine within the ETA Research Factory at the Technical University of Darmstadt.
Publisher
Springer International Publishing
Reference18 articles.
1. European Union: Complete energy balances (2022). https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do. Accessed 25 Mar 2022
2. Kara, S., Bogdanski, G., Li, W.: Electricity metering and monitoring in manufacturing systems. In: Hesselbach, J., Herrmann, C. (eds.) Glocalized Solutions for Sustainability in Manufacturing, pp. 1–10. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19692-8_1
3. Petruschke, L., Elserafi, G., Ioshchikhes, B., Weigold, M.: Machine learning based identification of energy efficiency measures for machine tools using load profiles and machine specific meta data. MM SJ (2021)
4. Mićković, A.: Energy metering and management practices of manufacturing companies: a systematic literature review. In: IAEE (2017)
5. Fresner, J., Morea, F., Krenn, C., Aranda Uson, J., Tomasi, F.: Energy efficiency in small and medium enterprises: Lessons learned from 280 energy audits across Europe. J. Clean. Prod. (2017)