Procedure Model for Data Driven Decision Support Regarding the Integration of Renewable Energies into Industrial Energy Management
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 casespecific 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. Keywords Data analytics, green production, industrial energy management, optimization, renewable energies, simulation. I. MODERN INDUSTRIAL ENERGY MANAGEMENT HE great approval of the participants of the United Nations Climate Change Conference in 2015 seemed to indicate that the main part of the world is aware of the necessity to change the way that we handle energy. There have already been lots of efforts to reduce greenhouse gas emissions in several countries. One example is the energy turnaround in Germany, which is one large project to break well known structures and patterns of a whole society on a scale of only a few decades. However, there is still a need to establish more extensive concepts for green industrial technologies and sustainable production to overcome the challenges of a new climate policy. [1] It is necessary to use synergies of different streams to tackle these biggest challenges of our society. The role of industry in the sustainability movement is specifically interesting. While renewable energies are on the rise and cities are M. Graus is with the Information Management Department, FIR at RWTH Aachen University, 52074 Aachen, Germany (phone: +49241 47705 514; email: Marcel.Graus@fir.rwth aachen.de) K. Westhoff is with the Technical Communication and Computer Science Department, RWTH University, 52056 Aachen, Germany (e mail: katalin.westhoff@rwth aachen.de) X. Xu is with the Industrial Engineering Department, Zhejiang University, Hangzhou, Zhejiang, 310027, China (e mail: xuxin1991@zju.edu.cn) reworked/modernized with the goal of energy efficiency, the inclusion of the industry in the so called future smart grid seems to be a major open task. The idea of a demand side management is quite old, but there are only a few successful examples put into practice so far. However, industry is not only influenced by the sustainability movement. A further central influence on the industry is the development towards the internet of things and the fourth industrial revolution or how it is called in Germany: Industrie 4.0. There is a natural synergy of the energy turnaround and Industrie 4.0 within the area of industrial energy management. [2] The energy management tends to include more and more data enabling the establishment of new concepts in production management. In this article, a procedure for data analytics to include the aspect of renewable energies into the industrial energy management will be introduced. In general, there are three dimensions of management: normative, strategic, and operational management. [3] In energy management, the operational dimension is very interesting and complex since it is closely correlated to the production management itself. Currently operational energy management in industry ends mostly with a monitoring of the energy consumption and possible measures like invest into more energy efficient machines and buildings, which are more strategic decision. The integration of energy efficiency in operational processes is a much more complicated topic that is mainly been avoided. However, there are a number of scientific works about demand side management and analytics of energy consumption data in manufacturing. The integration of energy consumption costs into the production scheduling on the example of a single machine was focused in [4] while [5] shows a Key Performance Indicators (KPI) based approach. One weakness of common concepts is that they are mainly knowledge driven in the sense that an expert builds a model that solves the problem in a theoretical environment. One characteristic example is the neglect of the gap between production planning and the actual production process. In contrast to a knowledge driven approach one can develop datadriven concepts, which become increasingly popular in many parts of the economy due to the digitalization [6] As a Procedure Model for Data Driven Decision Support Regarding the Integration of Renewable Energies into Industrial Energy Management M. Graus, K. Westhoff, X. Xu T World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:11, No:1, 2017 25 International Scholarly and Scientific Research Innovation 11(1) 2017 scholar.waset.org/1999.4/10006051 In te rn at io na l S ci en ce I nd ex C om pu te r an d In fo rm at io n E ng in ee ri ng V ol :1 1, N o: 1, 2 01 7 w as et .o rg /P ub lic at io n/ 10 00 60 51 consequence, for the modelling, the general conditions are determined a priori by an expert, and the main properties of the model are deduced from the empirical data without further influence from any expert. Moreover, most available works do not focus on a complete adaptive process that performs the transformation from a data basis to information used for a decision support. [7] In contrast, the focus in this article is on the process itself and resign a detailed explanation of the techniques used for each and every step. In order to simplify the individual steps, a consideration of only one simple machine is preferred, although the introduced concept can be applied for more complex machines and full processes as well. The application of the process for strategic energy management decisions in industry should help to estimate and release the potential of sustainable production concepts. The first barrier on this way is the need to convince the decision maker in strategic management decisions based on facts that are specific for the regarded company. The example of the integration of fluctuating energy prices in the production planning and control is not well understood so far, since today's investigations are mostly hypothetical studies, while a case specific study is needed to convince a decision maker in an enterprise. Thus, a suitable procedure to realize the idea of case specific analysis of the potential becomes necessary.