Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns
Ammonia (NH<sub>3</sub>) and carbon dioxide (CO<sub>2</sub>) 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 si...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/14/11/1891 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850149654738501632 |
|---|---|
| author | Yidan Xu Guanghui Teng Zhenyu Zhou |
| author_facet | Yidan Xu Guanghui Teng Zhenyu Zhou |
| author_sort | Yidan Xu |
| collection | DOAJ |
| description | Ammonia (NH<sub>3</sub>) and carbon dioxide (CO<sub>2</sub>) 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<sub>2</sub> 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<sub>2</sub> levels under different environmental regulation modes and data volumes. However, its prediction accuracy for NH<sub>3</sub> was slightly lower than that of the STL-GC-LSTM model. This may be due to the limited variability and regularity of the NH<sub>3</sub> 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. |
| format | Article |
| id | doaj-art-dd6ee35451114ea382fcd4351632144e |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-dd6ee35451114ea382fcd4351632144e2025-08-20T02:26:50ZengMDPI AGAgriculture2077-04722024-10-011411189110.3390/agriculture14111891Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding PatternsYidan Xu0Guanghui Teng1Zhenyu Zhou2College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, ChinaAmmonia (NH<sub>3</sub>) and carbon dioxide (CO<sub>2</sub>) 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<sub>2</sub> 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<sub>2</sub> levels under different environmental regulation modes and data volumes. However, its prediction accuracy for NH<sub>3</sub> was slightly lower than that of the STL-GC-LSTM model. This may be due to the limited variability and regularity of the NH<sub>3</sub> 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.https://www.mdpi.com/2077-0472/14/11/1891granger causalityXGBoostLSTMventilation |
| spellingShingle | Yidan Xu Guanghui Teng Zhenyu Zhou Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns Agriculture granger causality XGBoost LSTM ventilation |
| title | Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns |
| title_full | Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns |
| title_fullStr | Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns |
| title_full_unstemmed | Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns |
| title_short | Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns |
| title_sort | short term prediction method for gas concentration in poultry houses under different feeding patterns |
| topic | granger causality XGBoost LSTM ventilation |
| url | https://www.mdpi.com/2077-0472/14/11/1891 |
| work_keys_str_mv | AT yidanxu shorttermpredictionmethodforgasconcentrationinpoultryhousesunderdifferentfeedingpatterns AT guanghuiteng shorttermpredictionmethodforgasconcentrationinpoultryhousesunderdifferentfeedingpatterns AT zhenyuzhou shorttermpredictionmethodforgasconcentrationinpoultryhousesunderdifferentfeedingpatterns |