정보처리학회논문지:소프트웨어 및 데이터공학 (KIPS Transactions on Software and Data Engineering)

  • 제9권5호
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  • Pages.153-160
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  • 2020
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  • 2287-5905(pISSN)
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  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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데이터 마이닝 기반 스마트 공장 에너지 소모 예측 모델

An Energy Consumption Prediction Model for Smart Factory Using Data Mining Algorithms

  • ;
  • 이명배 (순천대학교 정보통신멀티미디어공학부) ;
  • 임종현 (순천대학교 정보통신멀티미디어공학부) ;
  • 김유빈 ((주)엘시스) ;
  • 신창선 (순천대학교 정보통신멀티미디어공학부) ;
  • 박장우 (순천대학교 정보통신멀티미디어공학부) ;
  • 조용윤 (순천대학교 정보통신멀티미디어공학부)
  • 투고 : 2019.12.31
  • 심사 : 2020.03.18
  • 발행 : 2020.05.31
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⟨ 이전 논문 다음 논문 ⟩

초록

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.

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.

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참고문헌

  1. C. Oluklulu, "A Research on the Photovoltaic Modules That Are Being Used Actively in Utilizing Solar Energy, Sizing of the Modules and Architectural Using Means of the Modules," Master's Thesis, Gazi University, Ankara, Turkey, 2001.
  2. V. I. Ugursal, "Energy Consumption, Associated Questions and some Answers," Appl. Energy, Vol. 130, pp. 783-792, 2014. https://doi.org/10.1016/j.apenergy.2013.11.072
  3. Rinkesh, What is the Energy Crisis. Available [Internet], https://www.conserve-energy-future.com/causesand-solutions-to-the-global-energy-crisis.php#abh_posts (accessed on 25 Jan. 2019).
  4. D. Streimikiene, "Residential Energy Consumption Trends, Main Drivers and Policies in Lithuania," Renew. Sustain. Energy Rev, Vol. 35, pp. 285-293, 2014. https://doi.org/10.1016/j.rser.2014.04.012
  5. J. Zuo and Z. Y. Zhao, "Green Building Research-Current Status and Future Agenda: A review. Renew. Sustain," Energy Rev., Vol. 30, pp. 271-281, 2014.
  6. Seung-Moon, Lee, "Mid-term Korea Energy Demand Outlook," Korea Energy Economics Institute, May 2014.
  7. L. Ekonomou, “Greek long-term energy consumption prediction using artificial neural networks,” Energy, Vol. 35, No. 2, pp. 512-517, 2010. https://doi.org/10.1016/j.energy.2009.10.018
  8. G. Munz, S. Li, and G. Carle, "Traffic Anomaly Detection Using k-means Clustering," In Proceedings of the GI/ITG Workshop MMBnet, Hamburg, Germany, pp. 13-14, Sep. 2007.
  9. K. Kandananond, “Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach,” Energies, Vol. 4, No. 12, pp. 1246-1257, 2011. https://doi.org/10.3390/en4081246
  10. C. De Cauwer, J. Van Mierlo, and T. Coosemans, “Energy Consumption Prediction for Electric Vehicles based on Real-world Data,” Energies, Vol. 8, No. 8, pp. 8573-8593, 2015. https://doi.org/10.3390/en8088573
  11. B. Dong, C. Cao, and S. E. Lee, “Applying Support Vector Machines to Predict Building Energy Consumption in Tropical Region,” Energy Build., Vol. 37, No. 5, pp. 545-553, 2005. https://doi.org/10.1016/j.enbuild.2004.09.009
  12. P. A. Gonzalez, and J. M. Zamarreno, “Prediction of Hourly Energy Consumption in Buildings Based on a Feedback Artificial Neural Network,” Energy Build., Vol. 37, No. 6, pp. 595-601, 2005. https://doi.org/10.1016/j.enbuild.2004.09.006
  13. B. B. Ekici and U. T. Aksoy, “Prediction of Building Energy Consumption by Using Artificial Neural Networks,” Adv. Eng. Softw., Vol. 40, No. 5, pp. 356-362, 2009. https://doi.org/10.1016/j.advengsoft.2008.05.003
  14. Q. Li, P. Ren, and Q. Meng, "Prediction Model of Annual Energy Consumption of Residential Buildings," In Proceedings of the 2010 International Conference on Advances in Energy Engineering, Beijing, China, pp. 223-226, 2010.
  15. L. Xuemei, D. Yuyan, D. Lixing, and J. Liangzhong, "Building Cooling Load Forecasting Using Fuzzy Support Vector Machine and Fuzzy C-mean Clustering," In Proceedings of the 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, Chengdu, pp. 438-441, 2010.
  16. Y. Ma, J. Q. Yu, C. Y. Yang, and L. Wang, "Study on Power Energy Consumption Model for Large-scale Public Building," In Proceedings of the 2010 2nd International Workshop on. IEEE Intelligent Systems and Applications, Wuhan, pp. 1-4, 2010.
  17. J. Zhao, Z. Han, W. Pedrycz, and W. Wang, “Granular model of long-term prediction for energy system in steel industry,” IEEE Transactions on Cybernetics, Vol. 46, No. 2, pp. 388-400, 2015. https://doi.org/10.1109/TCYB.2015.2445918
  18. Y. Zhang, X. Zhang, and L. Tang, "Energy consumption prediction in ironmaking process using hybrid algorithm of SVM and PSO," In International Symposium on Neural Networks, pp. 594-600, 2012.
  19. B. Zeng, M. Zhou, and J. Zhang, “Forecasting the energy consumption of China’s manufacturing using a homologous grey prediction model,” Sustainability, Vol. 9, No. 11, pp. 1-16, 2017.
  20. P. Sen, M. Roy, and P. Pal., “Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization,” Energy, Vol. 116, No. 1, pp. 1031-1038, 2016. https://doi.org/10.1016/j.energy.2016.10.068
  21. J. Reimann, "Methodology and model for predicting energy consumption in manufacturing at multiple scales," Procedia Manufacturing, Vol. 21, pp. 694-701, 2018. https://doi.org/10.1016/j.promfg.2018.02.173
  22. J. Zhou, E. Li, H. Wei, C. Li, Q. Qiao, and D. J. Armaghani, “Random forests and cubist algorithms for predicting shear strengths of rockfill materials,” Applied Sciences, Vol. 9, No. 8, pp. 1-16, 2019.
  23. J. D. Rodriguez, A. Perez, and J. A. Lozano, “Sensitivity analysis of k-fold cross validation in prediction error estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 3, pp. 569-575, 2009. https://doi.org/10.1109/TPAMI.2009.187
  24. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” In Ijcai, Vol. 14, No. 2, pp. 1137-1145, 1995.
  25. T. T. Wong, “Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation,” Pattern Recognition, Vol. 48, No. 9, pp. 2839-2846, 2015. https://doi.org/10.1016/j.patcog.2015.03.009
  26. R. C. Team, "R: A language and environment for statistical computing," 201, 2013.