A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO<sub<2</sub< Concentration as an Example

In a layer house, the CO<sub<2</sub< (carbon dioxide) concentration above the upper limit can cause the oxygen concentration to be below the lower limit suitable for poultry. This leads to chronic CO<sub<2</sub< poisoning in layers, which manifests as listlessness, reduced ap...
Ausführliche Beschreibung

In a layer house, the CO<sub<2</sub< (carbon dioxide) concentration above the upper limit can cause the oxygen concentration to be below the lower limit suitable for poultry. This leads to chronic CO<sub<2</sub< poisoning in layers, which manifests as listlessness, reduced appetite, weak constitution, decreased production performance, and weakened resistance to disease. Regulating ventilation may ensure a suitable CO<sub<2</sub< concentration in layer houses. Predicting the changes in CO<sub<2</sub< concentration and regulating the CO<sub<2</sub< concentration in advance are key to ensuring healthy large-scale breeding of layers. In recent years, machine learning and deep learning methods have been increasingly applied to this field. A CO<sub<2</sub< prediction model for layer house is proposed based on a GRU (gated recurrent unit) and LSTM (long short-term memory). The temperature, humidity, and CO<sub<2</sub< were determined as inputs to the model by the correlation coefficient. The datasets of the experimental layer house were continuously measured during June–July 2023, using a self-developed environmental monitor, and the monitored data were used as samples for model inputs. There were 22,000 time series data in the datasets. In this study, multivariate time series data were standardized via data pre-processing to improve model training. GRU and LSTM models were constructed. The models were trained using a training set. Then, these trained models were used to provide predictions on a test set. The prediction errors were calculated using the true values of the test set and the predicted values provided by the models. To test the performance of the model and accuracy of the predictions, predictions were made for different numbers of datasets. The results demonstrated that the combined prediction model had good generalization, stability, and convergence with high prediction accuracy. Due to the structure of the model, the stability of the LSTM model was higher than that of the GRU model, and its prediction accuracy and speed were lower than those of the GRU model. When the datasets of the GRU model were 15,000 to 17,000, The MAE of the GRU was 70.8077 to 126.7029 ppm, and the prediction time of the GRU is 16 to 24 ms. When the LSTM model’s datasets were 15,000–20,000, the MAE of LSTM was 78.8596 to 136.0896 ppm, and the prediction time of the GRU was 17 to 26 ms. Ausführliche Beschreibung