Mendia, IzaskunGil-Lopez, SergioDel Ser, JavierGrau, IñakiLejarazu, AdelaidaMaqueda, ErikPerea, EugenioAnalide, CesarNovais, PauloCamacho, DavidYin, Hujun2020-10-27Mendia , I , Gil-Lopez , S , Del Ser , J , Grau , I , Lejarazu , A , Maqueda , E & Perea , E 2020 , An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments . in C Analide , P Novais , D Camacho & H Yin (eds) , unknown . vol. 12490 , 0302-9743 , Springer , pp. 92-103 , 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 , Guimaraes , Portugal , 4/11/20 . https://doi.org/10.1007/978-3-030-62365-4_9conference978-3-030-62365-4; 978-3-030-62364-79783030623647researchoutputwizard: 11556/1032Publisher Copyright: © 2020, Springer Nature Switzerland AG.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 EU19382002.4-120.122134952enginfo:eu-repo/semantics/restrictedAccessconference output10.1007/978-3-030-62365-4_9Energy efficiencySmart manufacturingIntelligent systemsIndustry 4.0Big dataCyber physical systemsEnergy efficiencySmart manufacturingIntelligent systemsIndustry 4.0Big dataCyber physical systemsTheoretical Computer ScienceGeneral Computer ScienceSDG 7 - Affordable and Clean EnergySDG 9 - Industry, Innovation, and InfrastructureFunding InfoThis 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.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.http://www.scopus.com/inward/record.url?scp=85097184157&partnerID=8YFLogxK