IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1710-d1228552.html
   My bibliography  Save this article

Prediction Model of Pigsty Temperature Based on ISSA-LSSVM

Author

Listed:
  • Yuqing Zhang

    (College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Weijian Zhang

    (College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Chengxuan Wu

    (College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Fengwu Zhu

    (College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Zhida Li

    (College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China)

Abstract

The internal temperature of the pigsty has a great impact on the pigs. Keeping the temperature in the pigsty within a certain range is a pressing problem in environmental control. The current pigsty temperature regulation method is based mainly on manual and simple automatic control. There is rarely intelligent control, and such direct methods have problems such as low control accuracy, high energy consumption and untimeliness, which can easily lead to the occurrence of heat stress conditions. Therefore, this paper proposed an improved sparrow search algorithm (ISSA) based on a multi-strategy improvement to optimize the least squares support vector machine (LSSVM) to form a pigsty temperature prediction model. In the optimization process of the sparrow search algorithm (SSA), the initial position of the sparrow population was first generated by using the reverse good point set; secondly, the population number update formula was proposed to automatically adjust the number of discoverers and followers based on the number of iterations to improve the search ability of the algorithm; finally, the adaptive t-distribution was applied to the discoverer position variation to refine the discoverer population and further improve the search ability of the algorithm. Tests were conducted using 23 benchmark functions, and the results showed that ISSA outperformed SSA. By comparing it with the LSSVM models optimized by four standard algorithms, the prediction effect of the ISSA-LSSVM model was tested. In the end, the ISSA-LSSVM temperature prediction model had MSE of 0.0766, MAE of 0.2105, and R 2 of 0.9818. The results showed that the proposed prediction model had the best prediction performance and prediction accuracy, and can provide accurate data support for the prediction and control of the internal temperature of the pigsty.

Suggested Citation

  • Yuqing Zhang & Weijian Zhang & Chengxuan Wu & Fengwu Zhu & Zhida Li, 2023. "Prediction Model of Pigsty Temperature Based on ISSA-LSSVM," Agriculture, MDPI, vol. 13(9), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1710-:d:1228552
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1710/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/9/1710/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    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. Jakub Waikat & Amel Jelidi & Sandro Lic & Georgios Sopidis & Olaf Kähler & Anna Maly & Jesús Pestana & Ferdinand Fuhrmann & Fredi Belavić, 2024. "First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers," Energies, MDPI, vol. 17(5), pages 1-26, February.
    2. Fengwu Zhu & Yuqing Zhang & Weijian Zhang & Tianshi Gao & Suyu Wang & Lina Zhou, 2024. "Research on Predictive Control Method of Pigsty Environment Based on Fuzzy Control," Agriculture, MDPI, vol. 14(7), pages 1-18, June.

    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. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    2. Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
    3. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.
    4. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
    5. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    6. Haibo Huo & Jiajie Chen & Ke Wang & Fang Wang & Guangzhe Jin & Fengxiang Chen, 2023. "State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    7. Ge, Dongdong & Jin, Guiyang & Wang, Jianqiang & Zhang, Zhendong, 2024. "A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries," Energy, Elsevier, vol. 305(C).
    8. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    9. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).
    10. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
    11. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
    12. Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    13. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Tang, Aihua & Kan, Jiarong & Pecht, Michael, 2024. "SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion," Energy, Elsevier, vol. 308(C).
    14. Nanlan Wang & Xiangyang Xia & Xiaoyong Zeng, 2023. "State of charge and state of health estimation strategies for lithium-ion batteries," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 443-448.
    15. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    16. Yixin Liu & Ao Lei & Chunyang Yu & Tengfei Huang & Yuanbin Yu, 2024. "An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles," Energies, MDPI, vol. 17(13), pages 1-22, July.
    17. Ouyang, Tiancheng & Gong, Yubin & Ye, Jinlu & Deng, Qiaoyang & Su, Yingying, 2025. "State co-estimation for lithium-ion batteries based on multi-innovations online identification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    18. Chang, Chun & Pan, Yaliang & Wang, Shaojin & Jiang, Jiuchun & Tian, Aina & Gao, Yang & Jiang, Yan & Wu, Tiezhou, 2024. "Fast EIS acquisition method based on SSA-DNN prediction model," Energy, Elsevier, vol. 288(C).
    19. Yongyou Nie & Yuhan Wang & Lu Li & Haolan Liao, 2023. "Literature Review on Power Battery Echelon Reuse and Recycling from a Circular Economy Perspective," IJERPH, MDPI, vol. 20(5), pages 1-28, February.
    20. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(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:gam:jagris:v:13:y:2023:i:9:p:1710-:d:1228552. 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.