IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v274y2023ics0360544223007703.html
   My bibliography  Save this article

Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow search algorithm

Author

Listed:
  • Qiao, Weibiao
  • Fu, Zonghua
  • Du, Mingjun
  • Nan, Wei
  • Liu, Enbin

Abstract

Accurate seasonal peak load (SPL) prediction of underground gas storage (UGS) is of great significance for enterprises to formulate scheduling plans. In this work, a novel two-stage model is proposed to predict it. First, improved complete ensemble empirical mode decomposition (ICEEMDAN) is used to decompose the seasonal peak load into several intrinsic mode function. Second, the highest frequency components are smoothed by using Gaussian smoothing (GS), and second highest frequency component is decomposed into several components by applying wavelet transform (WT). Third, long short-term memory (LSTM) optimized by improved sparrow search algorithm (ISSA) is utilized to predict them, and the prediction results of these components are recombined to obtain the final prediction results. Finally, the historical data of Wen 23 in the Zhongyuan gas storage group is taken as a case, and three key issues are discussed. The results: (1) Utilizing ICEEMDAN, GS, WT, and ISSA can effectively improve the prediction performance of the LSTM; (2) The prediction performance of one-step is better than that of multi-step; (3) The comprehensive error and stability evaluation index is reasonable. The following conclusion is reached: the established prediction model can be used as a reference for the development of the UGS scheduling platform.

Suggested Citation

  • Qiao, Weibiao & Fu, Zonghua & Du, Mingjun & Nan, Wei & Liu, Enbin, 2023. "Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow searc," Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223007703
    DOI: 10.1016/j.energy.2023.127376
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223007703
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127376?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. Opeyemi Lateef Usman & Ravie Chandren Muniyandi & Khairuddin Omar & Mazlyfarina Mohamad, 2021. "Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-27, February.
    2. Guimin Zhang & Yuxuan Liu & Tao Wang & Hao Zhang & Zhenshuo Wang, 2021. "Ground Subsidence Prediction Model and Parameter Analysis for Underground Gas Storage in Horizontal Salt Caverns," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, September.
    3. Wang, Tongtao & Yang, Chunhe & Wang, Huimeng & Ding, Shuanglong & Daemen, J.J.K., 2018. "Debrining prediction of a salt cavern used for compressed air energy storage," Energy, Elsevier, vol. 147(C), pages 464-476.
    4. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
    5. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    6. Li, Peng & Li, Yinping & Shi, Xilin & Zhao, Kai & Liu, Xin & Ma, Hongling & Yang, Chunhe, 2021. "Prediction method for calculating the porosity of insoluble sediments for salt cavern gas storage applications," Energy, Elsevier, vol. 221(C).
    7. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    8. Vo Thanh, Hung & Zamanyad, Aiyoub & Safaei-Farouji, Majid & Ashraf, Umar & Hemeng, Zhang, 2022. "Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites," Renewable Energy, Elsevier, vol. 200(C), pages 169-184.
    9. Jie Zhang & Feifei Fang & Wei Lin & Shusheng Gao & Yalong Li & Qi Li & Yi Yang, 2020. "Research on Injection-Production Capability and Seepage Characteristics of Multi-Cycle Operation of Underground Gas Storage in Gas Field—Case Study of the Wen 23 Gas Storage," Energies, MDPI, vol. 13(15), pages 1-17, July.
    10. Ali, Aliyuda, 2021. "Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns," Energy, Elsevier, vol. 229(C).
    11. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    12. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    Full references (including those not matched with items on IDEAS)

    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, Xiong & Liu, Wei & Jiang, Deyi & Qiao, Weibiao & Liu, Enbin & Zhang, Nan & Fan, Jinyang, 2021. "Investigation on the influences of interlayer contents on stability and usability of energy storage caverns in bedded rock salt," Energy, Elsevier, vol. 231(C).
    2. Li, Jinlong & Zhang, Ning & Xu, Wenjie & Naumov, Dmitri & Fischer, Thomas & Chen, Yunmin & Zhuang, Duanyang & Nagel, Thomas, 2022. "The influence of cavern length on deformation and barrier integrity around horizontal energy storage salt caverns," Energy, Elsevier, vol. 244(PB).
    3. Wei, Nan & Yin, Lihua & Li, Chao & Wang, Wei & Qiao, Weibiao & Li, Changjun & Zeng, Fanhua & Fu, Lingdi, 2022. "Short-term load forecasting using detrend singular spectrum fluctuation analysis," Energy, Elsevier, vol. 256(C).
    4. Fang, Yu & Jia, Chunhong & Wang, Xin & Min, Fan, 2024. "A fusion gas load prediction model with three-way residual error amendment," Energy, Elsevier, vol. 294(C).
    5. Li, Hang & Ma, Hongling & Zhao, Kai & Zhu, Shijie & Yang, Kun & Zeng, Zhen & Zheng, Zhuyan & Yang, Chunhe, 2024. "Parameter design of the compressed air energy storage salt cavern in highly impure rock salt formations," Energy, Elsevier, vol. 286(C).
    6. Xiao, Ludi & Zhou, Peng & Bai, Yang & Zhang, Kai, 2024. "Modeling the dynamic allocation problem of multi-service storage system with strategy learning," Energy, Elsevier, vol. 302(C).
    7. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Lu, Xinyi & Tu, Renfu & Liao, Qi & Xu, Ning & Xia, Yuheng, 2023. "A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction," Energy, Elsevier, vol. 263(PD).
    8. Longfeng Zhang & Xin Ma & Hui Zhang & Gaoxun Zhang & Peng Zhang, 2022. "Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom," Energies, MDPI, vol. 15(19), pages 1-26, October.
    9. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    10. Yong Wang & Nan Wei & Dejun Wan & Shouxi Wang & Zongming Yuan, 2019. "Numerical Simulation for Preheating New Submarine Hot Oil Pipelines," Energies, MDPI, vol. 12(18), pages 1-26, September.
    11. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    12. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
    13. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    14. Yang, Shenyao & Hu, Shilai & Qi, Zhilin & Qi, Huiqing & Zhao, Guanqun & Li, Jiqiang & Yan, Wende & Huang, Xiaoliang, 2024. "Experiment and prediction for dynamic storage capacity of underground gas storage rebuilt from hydrocarbon reservoir," Renewable Energy, Elsevier, vol. 222(C).
    15. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    16. Rundong Gong & Xiukun Wang & Lei Li & Kaikai Li & Ran An & Chenggang Xian, 2022. "Lattice Boltzmann Modeling of Spontaneous Imbibition in Variable-Diameter Capillaries," Energies, MDPI, vol. 15(12), pages 1-19, June.
    17. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    18. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    19. Rodrigo A. de Marcos & Derek W. Bunn & Antonio Bello & Javier Reneses, 2020. "Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks," Energies, MDPI, vol. 13(20), pages 1-14, October.
    20. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

    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:274:y:2023:i:c:s0360544223007703. 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.