Key factor identification for energy consumption analysis



Published Jul 8, 2014
Gabriela Medina-Oliva Alexandre Voisin Maxime Monnin Jean-Baptiste Leger Benoit Iung


Nowadays the economic, environmental and societal issues concerning energy consumption require a deeper understanding of the factors influencing it. The influencing factors could concern the technical characteristics of the systems, the operational conditions and usage of equipment, the environmental conditions, etc. To understand the main contributing factors a knowledge model with the influencing factors is formalized in the form of an ontology. This ontology model allows to distinguish in a general way the main concepts (i.e. factors) that show higher consumption  trends. This way, a preliminary analysis reflecting the key influencing factors could be perform in order to focus later on a deeper analysis with data mining techniques. This paper focuses on the  formalization
of an ontology model in the marine domain for energy consumption purposes. Then, the approach is illustrated with an example of a fleet of diesel engines.

How to Cite

Medina-Oliva, G., Voisin, A., Monnin, M., Leger, J.-B., & Iung, B. (2014). Key factor identification for energy consumption analysis. PHM Society European Conference, 2(1).
Abstract 108 | PDF Downloads 117



energy consumption, key factor analysis, knowledge structuring, qualitative analysis

Abdelaziz, E., Saidur, R., & Mekhilef, S. (2011). A review on energy saving strategies in industrial sector. Renewable and Sustainable Energy Reviews, 15(1), 150–168. Analysis techniques for system reliability – procedure for failure mode and effects analysis (fmea) (Tech. Rep. No. IEC 60812). (2006). International Electrotechnical Commission (IEC).
Gruber, T. (2009). Ontology. In Encyclopedia of database systems (p. 1963-1965).
Hepbasli, A., & Ozalp, N. (2003). Development of energy efficiency and management implementation in the turkish industrial sector. Energy Conversion and Management, 44(2), 231–249. Maintenance terminology (Tech. Rep. No. EN 13306). (2001). Association Franc¸aise de Normalisation (AFNOR).
Medina-Oliva, G., Voisin, A., Monnin, M., & Lger, J.-B. (2014). Predictive diagnosis based on a fleet-wide ontology approach. Knowledge-Based Systems(0), To Appear.
Saidur, R. (2010). A review on electrical motors energy use and energy savings. Renewable and Sustainable Energy Reviews, 14(3), 877–898.
Voisin, A., Medina-Oliva, G., Monnin, M., Leger, J.-B., & Iung, B. (2013, Oct). Fleet-wide diagnostic and prognostic assessment. In Annual Conference of the Prognostics and Health Management Society 2013. New Orleans, ´ Etats-Unis.
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