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Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns

Author

Listed:
  • Yidan Xu

    (College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Guanghui Teng

    (College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

  • Zhenyu Zhou

    (College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)

Abstract

Ammonia (NH 3 ) and carbon dioxide (CO 2 ) are the main gases that affect indoor air quality and the health of the chicken flock. Currently, the environmental control strategy for poultry houses mainly relies on real-time temperature, resulting in lag and singleness. Indoor air quality can be improved by predicting the change in CO 2 concentration and proposing an optimal control strategy. Combining the advantages of seasonal-trend decomposition using loess (STL), Granger causality (GC), long short-term memory (LSTM), and extreme gradient boosting (XGBoost), an ensemble method called the STL-GC-LSTM-XGBoost model is proposed. This model can set fast response prediction results at a lower cost and has strong generalization ability. The comparative analysis shows that the proposed STL-GC-LSTM-XGBoost model achieved high prediction accuracy, performance, and confidence in predicting CO 2 levels under different environmental regulation modes and data volumes. However, its prediction accuracy for NH 3 was slightly lower than that of the STL-GC-LSTM model. This may be due to the limited variability and regularity of the NH 3 dataset, which likely increased model complexity and decreased predictive ability with the introduction of XGBoost. Nevertheless, in general, the proposed integrated model still provides a feasible approach for gas concentration prediction and health-related risk control in poultry houses.

Suggested Citation

  • Yidan Xu & Guanghui Teng & Zhenyu Zhou, 2024. "Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns," Agriculture, MDPI, vol. 14(11), pages 1-23, October.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:1891-:d:1506523
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    References listed on IDEAS

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    1. Trull, Oscar & García-Díaz, J. Carlos & Peiró-Signes, A., 2022. "Multiple seasonal STL decomposition with discrete-interval moving seasonalities," Applied Mathematics and Computation, Elsevier, vol. 433(C).
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