IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v254y2022ipbs0360544222012853.html
   My bibliography  Save this article

Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining

Author

Listed:
  • Zhang, Xinru
  • Hou, Lei
  • Liu, Jiaquan
  • Yang, Kai
  • Chai, Chong
  • Li, Yanhao
  • He, Sichen

Abstract

Accurate energy consumption prediction of crude oil pipeline is the basis for energy management and control optimization of oil transportation enterprises. The energy consumption of crude oil pipeline is affected by many factors, which is difficult to predict accurately by mechanism model. Machine learning model is not suitable for small samples and its results lack physical significance. In this paper, mechanism is integrated into machine learning model. A new physically guided neural network (PGNN) is proposed, which is established based on the physical modeling process of energy consumption prediction. The key physical intermediate variables affecting energy consumption are taken as artificial neurons and added to the loss function. The whale optimization algorithm is used to optimize the parameters of the model. A crude oil pipeline in Northeast China is taken as the prediction object to compare different models. The prediction accuracies of PGNN for electric energy consumption and fuel consumption are 2.54% and 4.36%, which are higher than other models. The prediction results of PGNN are more closely correlated with variables that directly affect energy consumption, which proves that PGNN has better physical consistency. In the case of small samples, PGNN has the least decline in accuracy. This study proves the feasibility of PGNN in energy consumption prediction of crude oil pipeline, and provides a new perspective for energy consumption prediction.

Suggested Citation

  • Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012853
    DOI: 10.1016/j.energy.2022.124382
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222012853
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124382?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Xi & Yu, Ruyi & Ullah, Sajid & Wu, Dianming & Li, Zhiqiang & Li, Qingli & Qi, Honggang & Liu, Jihui & Liu, Min & Zhang, Yundong, 2022. "A novel loss function of deep learning in wind speed forecasting," Energy, Elsevier, vol. 238(PB).
    2. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    3. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    4. Xu, Xiaofeng & Wang, Chenglong & Zhou, Peng, 2021. "GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    6. Yahya, Salah I. & Aghel, Babak, 2021. "Estimation of kinematic viscosity of biodiesel-diesel blends: Comparison among accuracy of intelligent and empirical paradigms," Renewable Energy, Elsevier, vol. 177(C), pages 318-326.
    7. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    8. Sun, Hongyue & Ebadi, Abdol Ghaffar & Toughani, Mohsen & Nowdeh, Saber Arabi & Naderipour, Amirreza & Abdullah, Aldrin, 2022. "Designing framework of hybrid photovoltaic-biowaste energy system with hydrogen storage considering economic and technical indices using whale optimization algorithm," Energy, Elsevier, vol. 238(PA).
    9. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    10. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    11. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    12. Hanson, Paul C. & Stillman, Aviah B. & Jia, Xiaowei & Karpatne, Anuj & Dugan, Hilary A. & Carey, Cayelan C. & Stachelek, Joseph & Ward, Nicole K. & Zhang, Yu & Read, Jordan S. & Kumar, Vipin, 2020. "Predicting lake surface water phosphorus dynamics using process-guided machine learning," Ecological Modelling, Elsevier, vol. 430(C).
    13. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    14. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    15. Paul Raccuglia & Katherine C. Elbert & Philip D. F. Adler & Casey Falk & Malia B. Wenny & Aurelio Mollo & Matthias Zeller & Sorelle A. Friedler & Joshua Schrier & Alexander J. Norquist, 2016. "Machine-learning-assisted materials discovery using failed experiments," Nature, Nature, vol. 533(7601), pages 73-76, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Zherui & Dai, Sining & Chen, Cong & Lyu, Huangwu & Zhang, Shuheng & Liu, Xuanji & Li, Yanghui, 2024. "Hydrate aggregation in oil-gas pipelines: Unraveling the dual role of asphalt and water," Energy, Elsevier, vol. 290(C).
    2. Wang, Chen & Zhou, Dengji & Wang, Xiaoguo & Liu, Song & Shao, Tiemin & Shui, Chongyuan & Yan, Jun, 2024. "Multiscale graph based spatio-temporal graph convolutional network for energy consumption prediction of natural gas transmission process," Energy, Elsevier, vol. 307(C).
    3. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).
    4. Xie, Yiwei & Li, Hongying & Xu, Miaomiao & Su, Yang & Zhang, Chaoyue & Han, Shanpeng & Zhang, Jinjun, 2023. "Effect of shear on durability of viscosity reduction of electrically-treated waxy crude oils," Energy, Elsevier, vol. 284(C).
    5. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Chen & Zhou, Dengji & Wang, Xiaoguo & Liu, Song & Shao, Tiemin & Shui, Chongyuan & Yan, Jun, 2024. "Multiscale graph based spatio-temporal graph convolutional network for energy consumption prediction of natural gas transmission process," Energy, Elsevier, vol. 307(C).
    2. Leng, Chunyang & Jia, Mingxing & Zheng, Haijin & Deng, Jibin & Niu, Dapeng, 2023. "Dynamic liquid level prediction in oil wells during oil extraction based on WOA-AM-LSTM-ANN model using dynamic and static information," Energy, Elsevier, vol. 282(C).
    3. Xiwen Cui & Shaojun E & Dongxiao Niu & Dongyu Wang & Mingyu Li, 2021. "An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target," Sustainability, MDPI, vol. 13(15), pages 1-21, August.
    4. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).
    5. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
    6. Zhang, Chu & Ji, Chunlei & Hua, Lei & Ma, Huixin & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction," Renewable Energy, Elsevier, vol. 197(C), pages 668-682.
    7. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    8. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    9. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    10. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    11. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    12. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    13. Guo, Yating & Rosland, Anitha & Ishak, Suryati & Muhammad Senan, Mohammad Khair Afham, 2023. "Public spending and natural resources development: A way toward green economic growth in China," Resources Policy, Elsevier, vol. 86(PB).
    14. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    15. Sajad Koochakinia & Amir Ebrahimi-Moghadam & Mahdi Deymi-Dashtebayaz, 2022. "Techno-Environmental Analyses and Optimization of a Utility Boiler Based on Real Data," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    16. Davoudkhani, Iraj Faraji & Dejamkhooy, Abdolmajid & Nowdeh, Saber Arabi, 2023. "A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging," Applied Energy, Elsevier, vol. 344(C).
    17. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    18. Laima Česonienė & Daiva Šileikienė & Vitas Marozas & Laura Čiteikė, 2021. "Influence of Anthropogenic Loads on Surface Water Status: A Case Study in Lithuania," Sustainability, MDPI, vol. 13(8), pages 1-15, April.
    19. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    20. Li, Zongxiang & Li, Liwei & Chen, Jing & Wang, Dongqing, 2024. "A multi-head attention mechanism aided hybrid network for identifying batteries’ state of charge," Energy, Elsevier, vol. 286(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012853. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.