Prediction Model of Carbon Dioxide Concentration in Pig House Based on Deep Learning

Abstract

The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment.

Harnessing the Power of Stacked GRU for Accurate Weather Predictions

“…The GRU model was chosen over other models for this analysis due to its advantages in weather prediction. GRU models have been widely used in various domains, including electric load forecasting [33], solar irradiance forecasting [34], precision agriculture [35], carbon dioxide concentration prediction [36], traffic prediction [37], landslide displacement prediction [38], wind speed and temperature forecasting [39], wildfire detection [40], and solar radiation prediction [41]. The advantages of GRU models in weather prediction include their simplicity and ease of implementation [33], ability to capture long-term dependencies in sequential data [34], improved performance with attention mechanisms [33], and computational efficiency compared to other recurrent neural network models [37].…”