IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v224y2021ics0360544221004023.html
   My bibliography  Save this article

Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression

Author

Listed:
  • Hu, Gang
  • Xu, Zhaoqiang
  • Wang, Guorong
  • Zeng, Bin
  • Liu, Yubing
  • Lei, Ye

Abstract

Predicting the energy consumption of oil pipelines is an important part of pipeline companies’ energy-saving and consumption-reduction plans and the realization of refined management. In order to predict the energy consumption of the long-distance product oil pipeline faster and better, this manuscript innovatively uses the normal distribution function to improve the search mode of the fruit fly optimization algorithm (FOA). It establishes the normal distribution fruit fly optimization algorithm (NFOA). It enhances search accuracy in the central area and effectively expands the search scope. Experimental results show that the accuracy and stability of the algorithm are improved by 100% and 900%. Then, NFOA combined with support vector regression (NFOA-SVR) is used to predict the three long-distance product pipeline data sets in China. The results show that the optimization speed and prediction accuracy of NFOA-SVR in LCY-Others set and LW-total set are significantly better than the other two algorithms. In the LCY-Pump set, NFOA-SVR has the same accuracy as the other two algorithms. Finally, experiments on random data sets show that the accuracy and stability of NFOA-SVR gradually decrease with the increase of the standard deviation of the data set.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004023
    DOI: 10.1016/j.energy.2021.120153
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221004023
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120153?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. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    2. 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.
    3. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    4. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
    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.
    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. 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).
    2. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
    3. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    4. Arya, Adarsh Kumar & Kumar, Adarsh & Pujari, Murali & Pacheco, Diego A.de J., 2023. "Improving natural gas supply chain profitability: A multi-methods optimization study," Energy, Elsevier, vol. 282(C).
    5. 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).
    6. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).
    7. 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.

    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. 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).
    2. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
    3. Shanbi Peng & Zhe Zhang & Yongqiang Ji & Laimin Shi, 2022. "Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm," Energies, MDPI, vol. 15(20), pages 1-19, October.
    4. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    5. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    6. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
    7. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    8. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    9. Mehrpooya, Mehdi & Ansarinasab, Hojat & Mousavi, Seyed Ali, 2021. "Life cycle assessment and exergoeconomic analysis of the multi-generation system based on fuel cell for methanol, power, and heat production," Renewable Energy, Elsevier, vol. 172(C), pages 1314-1332.
    10. Marquina, Jesús & Colinet, María José & Pablo-Romero, María del P., 2021. "The economic value of olive sector biomass for thermal and electrical uses in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    11. Sun, Yunpeng & Gao, Pengpeng & Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2023. "The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach," Energy, Elsevier, vol. 270(C).
    12. Yu, Xihong & Bao, Han & Chen, Mo & Bao, Bocheng, 2023. "Energy balance via memristor synapse in Morris-Lecar two-neuron network with FPGA implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    13. Ramos & Pablo Negri & Martín Breitkopf & María Laura Ojeda, 2021. "From International to Regional Commodity Price Pass-through Using Self-Driven Recurrent Networks," Asociación Argentina de Economía Política: Working Papers 4513, Asociación Argentina de Economía Política.
    14. Mok, Junghoon & Choi, Wonjung & Seo, Yongwon, 2021. "The dual-functional roles of N2 gas for the exploitation of natural gas hydrates: An inhibitor for dissociation and an external guest for replacement," Energy, Elsevier, vol. 232(C).
    15. Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
    16. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    17. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    18. Xiaojie Xu & Yun Zhang, 2022. "Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 169-181, July.
    19. Chen, Zhichao & Qiao, Yanyu & Guan, Shuo & Wang, Zhenwang & Zheng, Yu & Zeng, Lingyan & Li, Zhengqi, 2022. "Effect of inner and outer secondary air ratios on ignition, C and N conversion process of pulverized coal in swirl burner under sub-stoichiometric ratio," Energy, Elsevier, vol. 239(PD).
    20. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.

    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:224:y:2021:i:c:s0360544221004023. 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.