Determination of the typical load profile of industry tasks using fuzzy C-Means. (December 2020)

• Record Type: Journal Article
• Authors: Barreto, Rúben; Faria, Pedro; Vale, Zita
• Is Part Of: Energy reports. Volume 6(2020)Supplement 8
• Page Start: 155
• Page End: 160
• Publication Date: 2020-12
• Subjects: Clustering -- Data mining -- Fuzzy C-Means -- Typical load profile -- Unsupervised learning; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.11.094
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: This paper aims to promote the importance and advantages that the clustering method brings to the world of industry, making it possible to increase production efficiency and to manage the energy resources available better. The purpose of this paper is to group the consumption profiles of a task, in order to be able to determine which is the typical load profile of the task through the Fuzzy C-Means clustering method. The case study of this paper focuses on a task performed by three machines that make up a textile production line that makes several products. Each product, when going through a task performed by a specific machine, has a specific consumption and duration. Thus, by machine, it is determined which is the typical profile of ideal consumption to perform the designated task. In the same way, the general consumption profile of the task is highlighted, that is, the possible consumption profile to be expected when executing this task on one of the three machines.