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Prediction Model of Pigsty Temperature Based on ISSA-LSSVM

Author

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  • 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
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    References listed on IDEAS

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    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).
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    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.

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