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http://dx.doi.org/10.3745/KTCCS.2019.8.4.87

Machine Learning Approach for Pattern Analysis of Energy Consumption in Factory  

Sung, Jong Hoon ((주)에스더블유엠 부설연구소)
Cho, Yeong Sik ((주)AMEP 기술연구소)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.8, no.4, 2019 , pp. 87-92 More about this Journal
Abstract
This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).
Keywords
Factory Energy; Power Consumption; Machine Learning; Factory Energy Management System;
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