Jong Hoon Sung, Yeong Sik Cho

KIPS Transactions on Computer and Communication Systems, Vol. 8, No. 4, pp. 87-92, Apr. 2019
https://doi.org/10.3745/KTCCS.2019.8.4.87,   PDF Download:   Full Text
Keywords: Factory Energy, power consumption, Machine Learning, Factory Energy Management System
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).

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