{"title":"Procedure Model for Data-Driven Decision Support Regarding the Integration of Renewable Energies into Industrial Energy Management ","authors":"M. Graus, K. Westhoff, X. Xu","volume":121,"journal":"International Journal of Computer and Information Engineering","pagesStart":25,"pagesEnd":31,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10006051","abstract":"
The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.<\/p>\r\n","references":"[1]\tBundesministerium f\u00fcr Wirtschaft und Energie (BMWi), \u201eEnergie der Zukunft \u2013 Vierter Monitoring-Bericht zur Energiewende \u2013 Langfassung\u201c, online available at: https:\/\/www.bmwi.de\/DE\/Mediathek\/publikationen,did=739122.html, 2015, p. 4 et seq.\r\n[2]\tAcatech, Forschungsunion, \u201cUmsetzungsempfehlungen f\u00fcr das Zukunftsprojekt Industrie 4.0\u201d, online available at: https:\/\/www.bmbf.de\/files\/Umsetzungsempfehlungen_Industrie4_0.pdf, 2013, pp. 23-31.\r\n[3]\tK. Bleicher, \u201cDas Konzept Integriertes Management\u201d, Frankfurt\/Main, Campus Verlag Frankfurt\/New York, 2004, pp. 80-84. \r\n[4]\tFadi Shrouf, Joaquin Ordieres-Mer\u00e9, Alvaro Garc\u00eda-S\u00e1nchez, Miguel Ortega-Mier, \u201cOptimizing the production scheduling of a single machine to minimize total energy consumption costs\u201d, in Journal of Cleaner Production 67, 2014, pp. 197-207.\r\n[5]\tS. Bougain, D. Gerhard, C. Nigischer, S. Ugurlu, \u201cTowards energy management in production planning software based on energy consumption as a planning resource\u201d, in Procedia CIRP 26, 2015, pp. 139-144\r\n[6]\tM. J. Schniederjans, D. Schniederjans, C. M. Starkey, \u201eBusiness Analytics Principles, Concepts, and Applications: What, Why and How.\u201d, Upper Saddle River, New Jersey, Pearson Education, 2014, pp. 96-99.\r\n[7]\tN. Lin, \u201eApplied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics\u201d, Upper Saddle River, New Jersey, 2015, pp. XVI et seq.\r\n[8]\tM. Graus \u201cIntegration einer Datenanalytik in Energieinformationssysteme produzierender Unternehmen\u201d, in Smart Energy 2016, pp. 44-55.\r\n[9]\tM. Roscher, M. Graus, \u201cEnergiewende in der Industrie\u201d, in VDE Kongress 2016, Internet der Dinge, 07. -08.11.2016 in Mannheim, 2016.\r\n[10]\tR. Agrawal, K. Lin, H. S. Sawhney, K. Shim, \u201cFast similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases\u201d, in Proceedings of 21th International Conference on Very Large Data Bases. Zurich, 1995, pp. 490-500.\r\n[11]\tN. L\u00f6hndorf, M. Riel, S. Minner, \u201cSimulation Optimization for the Stochastic Economic Lot Scheduling Problem with Sequence-Dependent Setup Times\u201d, in International Journal of Production Economics, Volume 157, 2014, pp. 170-176.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 121, 2017"}