Temperature prediction of intensive pig house based on time series and multivariate models

ZENG Zhixiong; LUO Yizhi; YU Qiaodong; CAI Ren; LÜ Enli; XIA Jingjing

doi:10.7671/j.issn.1001-411X.202010008

Journal of South China Agricultural University > 2021 > 42(3): 111-118. > DOI: 10.7671/j.issn.1001-411X.202010008

ZENG Zhixiong, LUO Yizhi, YU Qiaodong, et al. Temperature prediction of intensive pig house based on time series and multivariate models[J]. Journal of South China Agricultural University, 2021, 42(3): 111-118. DOI: 10.7671/j.issn.1001-411X.202010008

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Temperature prediction of intensive pig house based on time series and multivariate models

1.
College of Engineering, South China Agricultural University, Guangzhou 510642, China
2.
Guangdong Mechanical & Electrical Polytechnic, Guangzhou 510515, China

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Received Date: October 12, 2020
Available Online: May 18, 2023

Abstract

Abstract

Objective
On account of history environmental factors such as temperature and humidity in farrowing houses, based on the data of a farrowing house of a modern pig farm in Guangdong Province, the temperature prediction model of pig house was studied based on time series and multivariate models.

Method
The effects of relative humidity, carbon dioxide concentration, oxygen concentration and other environmental factors on temperature prediction in pigsty was evaluated by statistical prediction of missing part environmental factors. Data preprocessing was carried out for the time series of piggery temperature to filter out the error value and missing value. The time series model was used to construct the temperature prediction model of piggery based on gated recurrent unit (GRU). Based on extreme gradient boosting (XGBoost), the temperature prediction model of missing value importance degree in pigsty was established using multivariate model. The prediction model was applied to predict the temperature of a farrowing house of an intensive pig farm in Guangdong Province, and compared with the recurrent neural network (RNN) model and back propagation neural network (BPNN) model.

Result
Comparing the predicted value with the measured value, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE) of the GRU model were 0.25 ℃, 0.19 ℃ and 0.65% respectively, while RMSE, MAE and MAPE of the XGBoost multicomponent model were 1.21 ℃, 0.71 ℃ and 2.50% respectively. For temperature prediction based on time series models, GRU model showed better prediction effect. The XGBoost model was better in temperature prediction for multivariate model.

Conclusion
The CRU model used in this study plays an early warning role on the temperature change of the farrowing house in time dimension, and the influence degree of various environmental parameters on temperature is also determined, which provides a reference for the fine regulation of the breeding environment.
- farrowing house,
- temperature prediction,
- time series model,
- multivariate model,
- feature importance

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References (38)

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XIA Jingjing

1.
College of Engineering, South China Agricultural University, Guangzhou 510642, China
2.
Guangdong Mechanical & Electrical Polytechnic, Guangzhou 510515, China

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Temperature prediction of intensive pig house based on time series and multivariate models

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