Hybrid virtual energy metering points – a low-cost energy monitoring approach for production systems based on offline trained prediction models
| Autor: | Johannes Sossenheimer, Oliver Vetter, Eberhard Abele, Matthias Weigold |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science Process (engineering) Industrial production 02 engineering and technology 010501 environmental sciences 01 natural sciences Industrial engineering 020901 industrial engineering & automation Resource (project management) Transparency (graphic) Component (UML) General Earth and Planetary Sciences Production (economics) Energy (signal processing) 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Procedia CIRP. 93:1269-1274 |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2020.04.128 |
| Popis: | With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. Virtual energy metering points, which use process data to predict the energy and resource demand, enable a cost-effective increase in energy transparency on machine level. In this paper, an approach based on offline trained neural networks is presented, through which the energy and resource consumption is continuously predicted for various production systems on machine and component level with high accuracy. Also the necessary data availability and the transferability to processes that are not included in the training dataset are discussed. |
| Databáze: | OpenAIRE |
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