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

Yidan Xu, Guanghui Teng () and Zhenyu Zhou
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Yidan Xu: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Guanghui Teng: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Zhenyu Zhou: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 11, 1-23

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.

Keywords: granger causality; XGBoost; LSTM; ventilation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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