IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i20p4350-d1263387.html
   My bibliography  Save this article

Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network

Author

Listed:
  • Zhiyuan Zhang

    (The College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Zhanshan Wang

    (The College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Accurate load forecasting can bring economic benefits and scheduling optimization. The complexity and uncertainty arising from the coupling of different energy sources in integrated energy systems pose challenges for simultaneously predicting multiple target load sequences. Existing data-driven methods for load forecasting in integrated energy systems use multi-task learning to address these challenges. When determining the input data for multi-task learning, existing research primarily relies on data correlation analysis and considers the influence of external environmental factors in terms of feature engineering. However, such feature engineering methods lack the utilization of the characteristics of multi-target sequences. In leveraging the characteristics of multi-target sequences, language generation models trained on textual logic structures and other sequence features can generate synthetic data that can even be applied to self-training to improve model performance. This provides an idea for feature engineering in data-driven time-series forecasting models. However, because time-series data are different from textual data, existing transformer-based language generation models cannot be directly applied to generating time-series data. In order to consider the characteristics of multi-target load sequences in integrated energy system load forecasting, this paper proposed a generative tractive network (GTN) model. By selectively utilizing appropriate autoregressive feature data for temporal data, this model facilitates feature mining from time-series data. This model is capable of analyzing temporal data variations, generating novel synthetic time-series data that align with the intrinsic temporal patterns of the original sequences. Moreover, the model can generate synthetic samples that closely mimic the variations in the original time series. Subsequently, through the integration of the GTN and autoregressive feature data, various prediction models are employed in case studies to affirm the effectiveness of the proposed methodology.

Suggested Citation

  • Zhiyuan Zhang & Zhanshan Wang, 2023. "Multi-Objective Prediction of Integrated Energy System Using Generative Tractive Network," Mathematics, MDPI, vol. 11(20), pages 1-18, October.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4350-:d:1263387
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/20/4350/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/20/4350/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    2. Zhang, Wenwen & Robinson, Caleb & Guhathakurta, Subhrajit & Garikapati, Venu M. & Dilkina, Bistra & Brown, Marilyn A. & Pendyala, Ram M., 2018. "Estimating residential energy consumption in metropolitan areas: A microsimulation approach," Energy, Elsevier, vol. 155(C), pages 162-173.
    3. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    4. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    5. Lei, Yang & Wang, Dan & Jia, Hongjie & Chen, Jingcheng & Li, Jingru & Song, Yi & Li, Jiaxi, 2020. "Multi-objective stochastic expansion planning based on multi-dimensional correlation scenario generation method for regional integrated energy system integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
    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. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    2. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    3. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    4. Chankook Park & Wan Gyu Heo & Myung Eun Lee, 2024. "Study on Consumers’ Perceived Benefits and Risks of Smart Energy System," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 288-300, May.
    5. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    6. Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).
    7. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    8. Paspuel Cristian & Luis Tipán, 2024. "Multi-Objetive Dispatching in Multi-Area Power Systems Using the Fuzzy Satisficing Method," Energies, MDPI, vol. 17(20), pages 1-36, October.
    9. Dong, Hanjiang & Zhu, Jizhong & Li, Shenglin & Wu, Wanli & Zhu, Haohao & Fan, Junwei, 2023. "Short-term residential household reactive power forecasting considering active power demand via deep Transformer sequence-to-sequence networks," Applied Energy, Elsevier, vol. 329(C).
    10. Liu, Shuwei & Tian, Jianyan & Ji, Zhengxiong & Dai, Yuanyuan & Guo, Hengkuan & Yang, Shengqiang, 2024. "Research on multi-digital twin and its application in wind power forecasting," Energy, Elsevier, vol. 292(C).
    11. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
    12. Liu, Liuchen & Cui, Guomin & Chen, Jiaxing & Huang, Xiaohuang & Li, Di, 2022. "Two-stage superstructure model for optimization of distributed energy systems (DES) part I: Model development and verification," Energy, Elsevier, vol. 245(C).
    13. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    14. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    15. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    16. Besagni, Giorgio & Borgarello, Marco & Premoli Vilà, Lidia & Najafi, Behzad & Rinaldi, Fabio, 2020. "MOIRAE – bottom-up MOdel to compute the energy consumption of the Italian REsidential sector: Model design, validation and evaluation of electrification pathways," Energy, Elsevier, vol. 211(C).
    17. Liu, Dewen & Luo, Zhao & Qin, Jinghui & Wang, Hua & Wang, Gang & Li, Zhao & Zhao, Weijie & Shen, Xin, 2023. "Low-carbon dispatch of multi-district integrated energy systems considering carbon emission trading and green certificate trading," Renewable Energy, Elsevier, vol. 218(C).
    18. Suryakiran, B.V. & Nizami, Sohrab & Verma, Ashu & Saha, Tapan Kumar & Mishra, Sukumar, 2023. "A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network," Energy, Elsevier, vol. 282(C).
    19. Semmelmann, Leo & Hertel, Matthias & Kircher, Kevin J. & Mikut, Ralf & Hagenmeyer, Veit & Weinhardt, Christof, 2024. "The impact of heat pumps on day-ahead energy community load forecasting," Applied Energy, Elsevier, vol. 368(C).
    20. Chao Zhang & Yihang Zhao & Huiru Zhao, 2022. "A Novel Hybrid Price Prediction Model for Multimodal Carbon Emission Trading Market Based on CEEMDAN Algorithm and Window-Based XGBoost Approach," Mathematics, MDPI, vol. 10(21), pages 1-16, November.

    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:gam:jmathe:v:11:y:2023:i:20:p:4350-:d:1263387. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.