The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
권호 표지
DOI QR코드

DOI QR Code

City Gas Pipeline Pressure Prediction Model

도시가스 배관압력 예측모델

  • Received : 2018.01.20
  • Accepted : 2018.04.20
  • Published : 2018.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

도시가스 배관은 지중에 매설되어 있기 때문에 세부 관리가 어렵고 다양한 위험에 노출되어 있다. 본 연구에서는 도시가스 배관압력 실시간 데이터를 분석해 배관압력 이상을 예측하고 전문가의 의사결정을 돕는 모델을 제안한다. 국내 도시가스 공급업체들 중 하나인 중부도시가스사의 정압기에서 수집하는 실시간 배관압력 데이터와 시간변수, 외부환경변수를 통합해 분석 데이터로 사용한다. 아산시와 천안시에 위치하는 11개 정압기를 분석 대상으로 하며 분 단위 배관압력 예측모델을 구현한다. Random forest, support vector regression(SVR), long-short term memory(LSTM) 알고리즘을 사용해 회귀모델을 구현한 결과 LSTM 모델에서 우수한 성능을 보인다. 아산시 배관압력 예측모델의 경우 LSTM 모델에서 RMSE가 0.011, MAPE가 0.494이며, 천안시 배관압력 예측모델의 경우 LSTM 모델에서 평균제곱근오차(root mean square error, RMSE)가 0.015, 절대평균백분율오차(mean absolute percentage error, MAPE)가 0.668로 가장 낮은 오류율을 보인다.

Keywords

References

  1. Akram, N. A., Isa, D., Rajkumar, R., and Lee, L. H., "Active incremental Support Vector Machine for oil and gas pipeline defects prediction system using long range ultrasonic transducers," Ultrasonics, Vol. 54, No. 6, pp. 1534-1544, 2014. https://doi.org/10.1016/j.ultras.2014.03.017
  2. El-Abbasy, M. S., Senouci, A., Zayed, T., Mirahadi, F., and Parvizsedghy, L., "Artificial neural network models for predicting condition of offshore oil and gas pipelines," Automation in Construction, Vol. 45, pp. 50-65, 2014. https://doi.org/10.1016/j.autcon.2014.05.003
  3. Hochreiter, S. and Schmidhuber, J., "Long short-term memory," Neural computation, Vol. 9, No. 8, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  4. Hwang, K., Mandayam, S., Udpa, S. S., Udpa, L., Lord, W., and Atzal, M., "Characterization of gas pipeline inspection signals using wavelet basis function neural networks," NDT & E International., Vol. 33, pp. 531-545, 2000. https://doi.org/10.1016/S0963-8695(00)00008-6
  5. Korean Oil Corporation's Petronet: http://www.petronet.co.kr/v3/index.jsp.
  6. Layouni, M., Hamdi, M. S., and Tahar, S., "Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning" Applied Soft Computing, Vol. 52, pp. 247-261, 2017. https://doi.org/10.1016/j.asoc.2016.10.040
  7. Lee, J. Y., Afzal, M., Udpa, S., Udpa, L., and Massopust, P., "Hierarchical rule based classification of MFL signals obtained from natural gas pipeline inspection," Neural Networks. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on IEEE., Vol. 5, pp. 71-76, 2000.
  8. Lee, L. H., Rajkumar, R., Lo, L. H., Wan, C. H., and Isa, D., "Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach," Expert Systems with Applications, Vol. 40 No. 6, pp. 1925-1934, 2013. https://doi.org/10.1016/j.eswa.2012.10.006
  9. Li, J., Fan, X., Chen, G., Gao, Z., Chen, M., and Li, L., "A DNN for small leakage detection of positive pressure gas pipelines in the semiconductor manufacturing," In Online Analysis and Computing Science (ICOACS), IEEE International Conference, IEEE, pp. 384-388, 2016.
  10. Liu, H., Liu, D., Zheng, G., Liang, Y., and Ni, Y., "Research on natural gas load forecasting based on support vector regression," In Intelligent Control and Automation, WCICA 2004, Fifth World Congress on IEEE, Vol. 4, pp. 3591A-3595, 2004.
  11. Mandal, S. K., Chan, F. T., and Tiwari, M. K., "Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM," Expert Systems with Applications, Vol. 39, No. 3, pp. 3071-3080, 2012. https://doi.org/10.1016/j.eswa.2011.08.170
  12. Min, G. Y. and Jeong, D. H., "Research on assessment of impact of big data attributes to disaster response decision-making process," Journal of Society for e-Business Studies, Vol. 18, No. 3, pp. 17-43, 2013. https://doi.org/10.7838/jsebs.2013.18.3.017
  13. Mohamed, A., Hamdi, M. S., and Tahar, S., "A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines," In Intelligent Systems and Applications. Springer International Publishing, pp. 1-18, 2016.
  14. Mohamed, A., Hamdi, M. S., and Tahar, S., "A machine learning approach for big data in oil and gas pipelines," In Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on IEEE, pp. 585-590, 2015.
  15. National Statistics Portal: http://kosis.kr/index/index.jsp.
  16. National Weather Service Station: https://data.kma.go.kr/cmmn/main.do.
  17. Nejatian, I., Kanani, M., Arabloo, M., Bahadori, A., and Zendehboudi, S., "Prediction of natural gas flow through chokes using support vector machine algorithm," Journal of Natural Gas Science and Engineering, Vol. 18, pp. 155-163, 2014. https://doi.org/10.1016/j.jngse.2014.02.008
  18. Noh, S. C., "Analysis of factors affecting energy consumption and $CO_2$ emissions structure in household sector," Doctoral dissertation, Seoul National University, 2013.
  19. T developers: https://developers.sktelecom.com/.
  20. Witten, I. H. and Frank, E., "Data Mining: Practical machine learning tools and techniques," Morgan Kaufmann, 2005.
  21. Zhang, S. and Zhou, W., "System reliability of corroding pipelines considering stochastic process-based models for defect growth and internal pressure," International Journal of Pressure Vessels and Piping, Vol. 111, pp. 120-130, 2013.

Cited by

  1. 미래 기상정보를 사용하지 않는 LSTM 기반의 피크시간 태양광 발전량 예측 기법 vol.24, pp.4, 2019, https://doi.org/10.7838/jsebs.2019.24.4.119