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.