Analysis of Time Series of Gas Energy Consumption by Using Elman Recurrent Neural Network

• 영문명: 엘만 순환 신경망을 사용한 가스 에너지 사용량 시계열 데이터 분석
• 발행기관: 한국산업경영시스템학회
• 저자명: Sunhee Kim(김선희) Dongju Lee(이동주) Chang-Yong Lee(이창용)
• 간행물 정보: 『한국산업경영시스템학회 학술대회』2018년 춘계학술대회 논문집, 25~25쪽, 전체 1쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2018.05.12

영문 초록

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of gas energy consumption in an air handling unit. To this end, we consider the volatility of the time series and demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the gas consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the gas consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of “context units” in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the gas consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

키워드

Elman recurrent neural network time series gas energy consumption energy management system