FEMS 적용을 위한 LSTM 기반의 식품공장 에너지 소비 예측 모델의 최적화

• 영문명: Optimization of Energy Consumption Prediction Model of Food Factory based on LSTM for Application to FEMS
• 발행기관: 한국환경에너지공학회
• 저자명: 이형아 김동주 조우진 구재회
• 간행물 정보: 『환경에너지공학』제18권 제1호, 7~19쪽, 전체 13쪽
• 주제분류: 공학 > 환경공학
• 파일형태: PDF
• 발행일자: 2023.07.31

영문 초록

There is an increasing need for reducing greenhouse gas emissions and improving the efficiency of energy consumption. In particular, energy consumption in the domestic industrial sector accounts for a large portion of the total energy consumption and is on a steady increase; therefore, it is required to prepare measures to increase the efficiency of energy consumption. One of the measures can be FEMS, which has functions such as real-time monitoring of energy use, energy consumption analysis, and control of various facilities, is attracting attention. Prediction of energy consumption among the various functions is an important first step in properly designing energy supplies and factory operations. In this paper, a study on energy consumption prediction was conducted for the purpose of application to FEMS. Machine learning technique was used to obtain predicted values by training the predictive model using available data, and LSTM algorithm was applied. To select a model suitable for the target food factory, the validity of the prediction model according to the combination of hyper parameters and variables was analyzed with CvRMSE and R 2 . By applying the optimal combination, the average error rate and accuracy of the prediction were confirmed through comparison between the predicted and measured values

목차

1. 서 론

2. 이론적 배경

3. LSTM 예측 모델 개발 및 평가

4. 결론 및 고찰

키워드

FEMS Machine Learning LSTM Energy Prediction Optimization of Prediction Model