Abstract
The concern of the industrial sector about the increase of energy costs has stimulated the development of new strategies for the effective management of energy consumption in industrial setups. Along with this growth, the irruption and continuous development of digital technologies have generated increasingly complex industrial ecosystems. These ecosystems are supported by a large number of variables and procedures for the operation and control of industrial processes and assets. This heterogeneous technological scenario has made industries difficult to manage by traditional means. In this context, the disruptive potential of cyber physical systems is beginning to be considered in the automation and improvement of industrial services. Particularly, intelligent data-driven approaches relying on the combination of Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Internet of Things (IoT) and Data Analytics provide the intelligence needed to optimally operate these complex industrial environments. The work presented in this manuscript contributes to the definition of the aforementioned intelligent data-driven approaches, defining a systematic, intelligent procedure for the energy efficiency diagnosis and improvement of industrial plants. This data-based diagnostic procedure hinges on the analysis of data collected from industrial plants, aimed at minimizing energy costs through the continuous assessment of the production-consumption ratio of the plant (i.e. energy per piece or kg produced). The proposed methodology aims to support managers and energy-efficiency technicians to minimize the plant’s energy consumption without affecting the production and therefore, increase its competitiveness. The data used in the design of this methodology are real data from a company dedicated to the design and manufacture of automotive components and one of the main manufacturers in the automotive sector worldwide. The present methodology is under the pending patent application EU19382002.4-120.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antsaklis, P.: Goals and challenges in cyber-physical systems research editorial of the editor in chief. IEEE Trans. Autom. Control 59(12), 3117–3119 (2014)
Cheng, Y., Chen, K., Sun, H., Zhang, Y., Tao, F.: Data and knowledge mining with big data towards smart production. J. Ind. Inf. Integr. 9, 1–13 (2018)
Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0. Inf. Fusion, 50, 92–111 (2019)
EUROSTAT: Electricity prices for non-household consumers (2020). https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_205. Accessed 2 April 2020
Hamerly, G., Elkan, C.: Learning the k in k-means. In: Advances in Neural Information Processing systems, pp. 281–288 (2004)
Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., Ivanova, M.: A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int. J. Product. Res. 54(2), 36–402 (2016)
Lasi, H., Fettke, P., Kemper, H.G., Feld, T.: Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)
Li, H.X., Xu, L.D.: Feature space theory-a mathematical foundation for data mining. Knowl. Based Syst. 14(5–6), 253–257 (2001)
Ma, S., Zhang, Y., Lv, J., Yang, H., Wu, J.: Energy-cyber-physical system enabled management for energy-intensive manufacturing industries. J. Clean. Product. 226, 892–903 (2019)
Oztemel, E., Gursev, S.: Literature review of industry 4.0 and related technologies. J. Intell. Manufact. 31(1), 127–182 (2020)
Pereira, A., Romero, F.: A review of the meanings and the implications of the industry 4.0 concept. Proc. Manufact. 13, 1206–1214 (2017)
Pham, D.T., Dimov, S.S., Nguyen, C.D.: Selection of k in k-means clustering. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 219(1), 103–119 (2005)
Sheng, G., Zhao, X., Zhang, H., Lv, Z., Song, H.: Mathematical models for simulating coded digital communication: a comprehensive tutorial by big data analytics in cyber-physical systems. IEEE Access 4, 9018–9026 (2016)
Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D.: Towards industry 4.0-standardization as the crucial challenge for highly modular, multi-vendor production systems. Ifac-Papersonline, 48(3), 579–584 (2015)
Wielki, J.: The impact of the internet of things concept development on changes in the operations of modern enterprises. Polish J. Manage. Stud. 15 (2017)
Xu, L.D., Xu, E.L., Li, L.: Industry 4.0: state of the art and future trends. Int. J. Product. Res. 56(8), 2941–2962 (2018)
Zhou, K., Liu, T., Zhou, L.: Industry 4.0: towards future industrial opportunities and challenges. In: 2015 12th International conference on fuzzy systems and knowledge discovery (FSKD), pp. 2147–2152. IEEE (2015)
Acknowledgments
This work has received funding support from the HAZITEK program of the Basque Government (Spain) through the NAIA (Ref. ZL-2017/00701) research grants. It is also appreciate the deference of the company GESTAMP, especially to Iñaki Grau, to provide data from several of its plants. Finally, Javier Del Ser acknowledged funding support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government, as well as by ELKARTEK and EMAITEK programs of this same institution.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mendia, I. et al. (2020). An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-62365-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62364-7
Online ISBN: 978-3-030-62365-4
eBook Packages: Computer ScienceComputer Science (R0)