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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.

Energy efficiency of production systems and of the product itself has grown to a critical competitive factor. Besides the manufacturer’s monetary motivation there are increasing incentives to meet customers’ expectations regarding lifecycle cost and the ecological footprint of products. That neo-ecology, as one megatrend, leads to a new business moral resulting in an energy optimization of the whole product life cycle in terms of resource and energy input. There is a plenty of measures to reduce the energy consumption of a production system and thus to increase its efficiency. To do so companies do not have to develop proprietary solutions for their production sites but can draw on a large pool of measures. However, in practice, many energy optimization measures are unknown to their energy managers. This is mainly owing to the fact that there is no standardized categorization for energy optimization potentials yet. In addition, many efficiency deficits remain undetected as a result of a non-existing efficient methodology for finding energy consumption optimization measures. The domain of information retrieval addresses this issue, as it is able to provide documents matching the user’s information demand. Nevertheless, search queries have to be sufficiently well known in order to gain adequate results. In this paper we show how ontologies can be used to support the user in defining search queries and finding optimization measures efficiently. As formal and explicit specifications of shared conceptualizations, ontologies offer the possibility to represent relevant parts of knowledge in a standardized, machine-readable manner. Therefore, ontologies improve upon data models, which are mainly used for single applications. For the purpose of energy efficiency in production environments, we provide both a methodology to build ontologies for describing energy saving measures and illustrate the application for explicit energy efficiency optimization measures.

This paper presents a calculation system for evaluating the energy efficiency at machine, plant, location, company, and sector level based on the process specific minimum energy demand. The goal is a comparability of the energy efficiency across machines, plants, locations, companies, and sectors through definition of significant key figures. The basis of the derivation of possible saving potentials is the relative energy efficiency (REE). [7] It is determined by the quotient of minimal energy demand and actually measured consumption and requires that the actually measured energy consumption refers to an independent basis of comparison. The step-by-step development of the calculation system, structured in levels, is based on the detailed analysis of all the influential factors of the energy consumption with the help of cause and effect diagrams to calculate the minimally necessary energy demands for the manufacturing process. Furthermore, the described bottom-up approach delivers, ensuing from the process oriented level of perspective, the step-by-step conception of the calculation method. The REE of a level of perspective is calculated on the basis of the REE value of the previous production level as well as according weighting factors. On the basis of the calculation, as well as subsequent measurements within the company, optimization potentials [10] can be clearly described and can lead back to their roots. These optimization potentials are based on exemplary trials presented for a chosen manufacturing chain of the electronics production area.