[논문]데이터 마이닝 기반 스마트 공장 에너지 소모 예측 모델

이명배; 임종현; 김유빈; 신창선; 박장우; 조용윤

doi:10.3745/ktsde.2020.9.5.153

데이터 마이닝 기반 스마트 공장 에너지 소모 예측 모델
An Energy Consumption Prediction Model for Smart Factory Using Data Mining Algorithms 원문보기

정보처리학회논문지. KIPS transactions on software and data engineering. 소프트웨어 및 데이터 공학, v.9 no.5, 2020년, pp.153 - 160

(순천대학교 정보통신멀티미디어공학부) , 이명배 (순천대학교 정보통신멀티미디어공학부) , 임종현 (순천대학교 정보통신멀티미디어공학부) , 김유빈 ((주)엘시스) , 신창선 (순천대학교 정보통신멀티미디어공학부) , 박장우 (순천대학교 정보통신멀티미디어공학부) , 조용윤 (순천대학교 정보통신멀티미디어공학부)

초록
AI-Helper

산업용 에너지 소비 예측은 에너지 수요와 공급에 동적이고 계절적인 변화가 있기 때문에 에너지 관리 및 제어 시스템에서 중요한 위치를 차지한다. 본 논문은 철강 산업의 에너지 소비 예측 모델을 제시하고 논의한다. 사용되는 데이터에는 후행 및 선도적인 전류 반응 전력, 후행 및 선도적인 전류 동력 계수, 이산화탄소(TCO2) 배출 및 부하 유형이 포함된다. 테스트 세트에서는 (a) 선형 회귀(LR), (b) 방사형 커널(SVM RBF), (c) Gradient Boosting Machine (GBM), (d) 무작위 포리스트(RF). 평균 제곱 오차(RMSE), 평균 절대 오차(MAE) 및 평균 절대 백분율 오차(ME)의 네 가지 통계 모델을 사용하여 예측하고 평가한다. 회귀 설계의 효율성 모든 예측 변수를 사용할 때 최상의 모델 RF는 테스트 세트에서 RMSE 값 7.33을 제공할 수 있다.

Abstract ▼ AI-Helper

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

주제어

표/그림 (9)

그림 Fig. 1. Steel Industry Energy Consumption Measurement for 1 Year
그림 Fig. 2. First Five Weeks Hourly Steel Industry Energy Consumption Heat-map
표 Table 1. Load Type and Its Timings
표 Table 2. Data Variables and Description
그림 Fig. 3. RF with all the Parameters
그림 Fig. 4. Grid Search Outcomes for Optimal Values of Sigma and Cost Values for the SVM-Radial Model
표 Table 3. Training and Testing Set
그림 Fig. 5. Grid Search Outcomes for an Optimum Number of Trees (GBM Model Boosting Lterations and Maximum Tree Depth)
표 Table 4. Model Performance

AI 본문요약
AI-Helper

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문제 정의

This paper explores the potential for predicting energy consumption by data mining approaches. This study leads to the conclusion that RF is best in predicting the energy and GBM performance also equal to RF.
In the exploratory analysis the data analysis reveals thought-provoking results. This work aims to establish the best performing prediction algorithm to predict the hourly consumption of energy in the steel industry. The findings indicate that the RF model improves RMSE, MAE, and MAPE of predictions in consideration to other regression models considered in this research.

제안 방법

It is essential to find optimal tuning parameters for each of the regression algorithms for finding and reducing error values while designing a model. LR has no tuning parameters and the grid search is not performed for LR.
In this aspect, as indicated by Kohavi [24] and Wong [25], the number of folds K was set at ten, and correlated with the trade-off between the measurement time and the bias. Ten rounds of training and validation were therefore performed using different partitions, and then the results are summed to reflect the output of LR, SVM, GBM, and RF on the training set. In this study all data processing was done using R software [26].
A long-term energy consumption prediction is suggested in by using a granular computing approach integrating industrial-driven semantics and granulating initial data based on the specificity of manufacturing processes [17]. The authors used real-world industrial energy data from a Chinese steel plant to assess the efficiency of the proposed method. The findings reveal that the method proposed is better compared to some other data-driven methods.
This work aims to establish the best performing prediction algorithm to predict the hourly consumption of energy in the steel industry. The findings indicate that the RF model improves RMSE, MAE, and MAPE of predictions in consideration to other regression models considered in this research.
And, the experimental study was carried out on the basis of practical information from a Chinese Iron & Steel company. The proposed method can predict the energy consumption of the ironmaking process with adequate precision. On the basis of the Gray system theory, a homologous gray prediction model with one parameter and one first order equation (HGEM (1,1)) is proposed to estimate total Chinese production energy consumption [19].
This analysis focuses on energy usage (Kwh) information recorded for the industry every 1 hour. The data period is 365 days (2018, 12 months).

대상 데이터

The data is gathered from DAEWOO steel company in Gwangyang city, South Korea. The Industry produces several kinds of the coil, steel sheets and iron plates.

성능/효과

This paper explores the potential for predicting energy consumption by data mining approaches. This study leads to the conclusion that RF is best in predicting the energy and GBM performance also equal to RF. So, RF and GBM are more suitable for predicting steel industry energy consumption prediction.

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