Big data analytics methodologies applied at energy management in industrial sector: A case study.

In this work, a framework is developed to integrate IoT-based energy management and company's existing information systems. This framework is a multi-layer model that includes three layers: 1) data collection layer, 2) data management layer and 3) data analytics layer. In order to test the proposed approach and assess its impact on improving energy efficiency, a pilot studywas carried out in an Italian manufacturing company. Several smart meters have been installed at machine level to collect energy consumption data in real time, and then this data have been analyzed and provided to decision makers to improve energy efficiency by integrating them in production management decisions. When a company aims at analyzing the energy characteristics of its production system, data provided by different sources and geographically dispersed repositories must be taken into consideration. These characteristics bring several problems to develop a data analytic architecture. In this paper, we propose a data analytic model for IoT, in order to integrate the data collected from different sources and to improve energy-aware decision-making. Improving the overall equipment effectiveness of machine tools will improve resource-efficiency and productivity in manufacturing and support the development of smart factories from an energy point of view. [ABSTRACT FROM AUTHOR]

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