Semantic Meta Model for the Description of Resource and Energy Data in the Energy Data Management Cycle

Abstract:

Article Preview

Energy efficiency is a critical competitive factor. Transparency of energy consumption is the key for increasing efficiency of production. For this purpose, existing energy data management systems collect data such as power, gas or water consumption on field level, save them in databases, and aggregate them in reports. However, the identification of saving potentials and the definition of efficiency measures is carried out by energy experts and thus is dependent on a person’s knowledge. The documentation of knowledge about saving potentials and measures does not take place and relations among data and knowledge of various domains are not captured. In this paper, we provide an approach that allows the holistic capture and description of data and knowledge relations. Through the use of an ontology-based meta model, consumption data can be augmented with information about time and place of capture, data type, intended purpose and permissions, as well as interfaces to other systems and relations to knowledge elements. The semantic model is to capture relevant requirements of all information demanders within the energy data management cycle. Therefore, the model is capable of detecting efficiency deficits and retrieving relevant energy efficiency measures within a knowledge base. Thus, energy consumption data can be efficiently used and knowledge about efficiency can be sustainably preserved.

Info:

Periodical:

Edited by:

Jörg Franke, Sven Kreitlein, Gunther Reinhart, Christian Gebbe, Rolf Steinhilper and Johannes Böhner

Pages:

69-76

Citation:

M. Brandmeier et al., "Semantic Meta Model for the Description of Resource and Energy Data in the Energy Data Management Cycle", Applied Mechanics and Materials, Vol. 871, pp. 69-76, 2017

Online since:

October 2017

Export:

Price:

$41.00

* - Corresponding Author

[1] T. Javied, T. Rackow, R. Stankalla, C. Sterk, and J. Franke, A Study on Electric Energy Consumption of Manufacturing Companies in the German Industry with the Focus on Electric Drives, in 48th CIRP Conference on MANUFACTURING SYSTEMS, Ischia, Italy, (2015).

DOI: https://doi.org/10.1016/j.procir.2015.10.006

[2] B. Haarmann, Ontology On Demand: Vollautomatische Ontologieerstellung aus deutschen Texten mithilfe moderner Textmining-Prozesse. Berlin: epubli GmbH, (2014).

[3] R. Studer, V. Benjamins, and D. Fensel, Knowledge engineering: Principles and methods, Data & Knowledge Engineering, vol. 25, no. 1-2, p.161–197, (1998).

DOI: https://doi.org/10.1016/s0169-023x(97)00056-6

[4] Barry Smith, Beyond Concepts: Ontology as Reality Representation, in Proceedings of Formal Ontology in Information Systems (FOIS), A. Varzi and L. Vieu, Eds., (2004).

[5] H. Stuckenschmidt, Ontologien Konzepte, Technologien und Anwendungen, 2nd ed. Berlin: Springer, (2011).

[6] D. L. Mcguinness, R. Fikes, J. Rice, and S. Wilder, An Environment for Merging and Testing Large Ontologies, in Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000), Morgan Kaufmann Publisher, Ed., 2000, p.483.

[7] S. Beißel, Ontologiegestütztes Case-Based Reasoning: Entwicklung und Beurteilung semantischer Ähnlichkeitsindikatoren für die Wiederverwendung natürlichsprachlich repräsentierten Projektwissens, 1st ed. Wiesbaden: Gabler, (2011).

[8] H. Wicaksono and S. Rogalski, Ontology Supported Intelligent Energy Management System in Buildings, in The International Conference on Industrial Engineering and Business Management (ICIEBM) 2010: Proceedings, A. Wirabhuana and M. Abrori, Eds., 1st ed., Yogyakarta: UIN Sunan Kalijaga, 2010, p.637.

[9] N. Shah, K. -M. Chao, T. Zlamaniec, and A. Matei, Ontology for Home Energy Management Domain, in Communications in Computer and Information Science, Digital Information and Communication Technology and Its Applications, H. Cherifi, J. M. Zain, and E. El-Qawasmeh, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, p.337.

DOI: https://doi.org/10.1007/978-3-642-22027-2_28

[10] H. Wicaksono, F. Jost, S. Rogalski, and J. Ovtcharova, Energy efficiency evaluation in manufacturing through an ontology-represented knowledge base, Intell. Sys. Acc. Fin. Mgmt., vol. 21, no. 1, p.59–69, (2014).

DOI: https://doi.org/10.1002/isaf.1347

[11] W. K. Michener and M. B. Jones, Ecoinformatics: Supporting ecology as a data-intensive science, (eng), Trends in ecology & evolution, vol. 27, no. 2, p.85–93, (2012).

DOI: https://doi.org/10.1016/j.tree.2011.11.016

[12] N. F. Noy and D. L. Mcguinness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford Knowledge Systems Laboratory, Stanford University, Stanford (USA), (2001).

[13] M. Brandmeier, F. Schäfer, S. Kreitlein, and J. Franke, Ontology-based Description of Energy Optimization Potentials for Production Environments, in Energy efficiencey in strategy of sustainable production, J. Franke and S. Kreitlein, Eds., Pfaffikon: Trans Tech, 2015, p.53.

DOI: https://doi.org/10.4028/www.scientific.net/amm.805.53

[14] A. Hogan, Creating and using ontologies, ESWC Summer School, Creating and using ontologies, (2012).

[15] M. Gruninger and M. Fox, Methodology for the Design and Evaluation of Ontologies, Workshop on Basic Ontological Issues in Knowledge Sharing, (1995).

[16] Deutsche Nationalbibliothek, Metadaten & Struktur. [Online] Available: https: /wiki. dnb. de/pages/viewpage. action?pageId=95651769. Accessed on: May 11 (2017).

[17] M. Uschold and M. Gruninger, Ontologies: Principles, Methods and Applications, Knowledge Engineering Review, no. 11, (1996).