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 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

Gespeichert in:

Autor*in:	Xiaoyang Chen [verfasserIn] Lijia Yang [verfasserIn] Hao Xue [verfasserIn] Lihua Li [verfasserIn] Yao Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	layer house CO prediction GRU LSTM

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 24(2023), 1, p 244
Übergeordnetes Werk:	volume:24 ; year:2023 ; number:1, p 244

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s24010244

Katalog-ID:	DOAJ097769630

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allfields	10.3390/s24010244 doi (DE-627)DOAJ097769630 (DE-599)DOAJfe95c8a954db4803a57ad7406290dfc8 DE-627 ger DE-627 rakwb eng TP1-1185 Xiaoyang Chen verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. layer house CO<sub<2</sub< concentration prediction GRU LSTM Chemical technology Lijia Yang verfasserin aut Hao Xue verfasserin aut Lihua Li verfasserin aut Yao Yu verfasserin aut In Sensors MDPI AG, 2003 24(2023), 1, p 244 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2023 number:1, p 244 https://doi.org/10.3390/s24010244 kostenfrei https://doaj.org/article/fe95c8a954db4803a57ad7406290dfc8 kostenfrei https://www.mdpi.com/1424-8220/24/1/244 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1, p 244
spelling	10.3390/s24010244 doi (DE-627)DOAJ097769630 (DE-599)DOAJfe95c8a954db4803a57ad7406290dfc8 DE-627 ger DE-627 rakwb eng TP1-1185 Xiaoyang Chen verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. layer house CO<sub<2</sub< concentration prediction GRU LSTM Chemical technology Lijia Yang verfasserin aut Hao Xue verfasserin aut Lihua Li verfasserin aut Yao Yu verfasserin aut In Sensors MDPI AG, 2003 24(2023), 1, p 244 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2023 number:1, p 244 https://doi.org/10.3390/s24010244 kostenfrei https://doaj.org/article/fe95c8a954db4803a57ad7406290dfc8 kostenfrei https://www.mdpi.com/1424-8220/24/1/244 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1, p 244
allfields_unstemmed	10.3390/s24010244 doi (DE-627)DOAJ097769630 (DE-599)DOAJfe95c8a954db4803a57ad7406290dfc8 DE-627 ger DE-627 rakwb eng TP1-1185 Xiaoyang Chen verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. layer house CO<sub<2</sub< concentration prediction GRU LSTM Chemical technology Lijia Yang verfasserin aut Hao Xue verfasserin aut Lihua Li verfasserin aut Yao Yu verfasserin aut In Sensors MDPI AG, 2003 24(2023), 1, p 244 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2023 number:1, p 244 https://doi.org/10.3390/s24010244 kostenfrei https://doaj.org/article/fe95c8a954db4803a57ad7406290dfc8 kostenfrei https://www.mdpi.com/1424-8220/24/1/244 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1, p 244
allfieldsGer	10.3390/s24010244 doi (DE-627)DOAJ097769630 (DE-599)DOAJfe95c8a954db4803a57ad7406290dfc8 DE-627 ger DE-627 rakwb eng TP1-1185 Xiaoyang Chen verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. layer house CO<sub<2</sub< concentration prediction GRU LSTM Chemical technology Lijia Yang verfasserin aut Hao Xue verfasserin aut Lihua Li verfasserin aut Yao Yu verfasserin aut In Sensors MDPI AG, 2003 24(2023), 1, p 244 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2023 number:1, p 244 https://doi.org/10.3390/s24010244 kostenfrei https://doaj.org/article/fe95c8a954db4803a57ad7406290dfc8 kostenfrei https://www.mdpi.com/1424-8220/24/1/244 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1, p 244
allfieldsSound	10.3390/s24010244 doi (DE-627)DOAJ097769630 (DE-599)DOAJfe95c8a954db4803a57ad7406290dfc8 DE-627 ger DE-627 rakwb eng TP1-1185 Xiaoyang Chen verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. layer house CO<sub<2</sub< concentration prediction GRU LSTM Chemical technology Lijia Yang verfasserin aut Hao Xue verfasserin aut Lihua Li verfasserin aut Yao Yu verfasserin aut In Sensors MDPI AG, 2003 24(2023), 1, p 244 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:24 year:2023 number:1, p 244 https://doi.org/10.3390/s24010244 kostenfrei https://doaj.org/article/fe95c8a954db4803a57ad7406290dfc8 kostenfrei https://www.mdpi.com/1424-8220/24/1/244 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1, p 244
language	English
source	In Sensors 24(2023), 1, p 244 volume:24 year:2023 number:1, p 244
sourceStr	In Sensors 24(2023), 1, p 244 volume:24 year:2023 number:1, p 244
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	layer house CO<sub<2</sub< concentration prediction GRU LSTM Chemical technology
isfreeaccess_bool	true
container_title	Sensors
authorswithroles_txt_mv	Xiaoyang Chen @@aut@@ Lijia Yang @@aut@@ Hao Xue @@aut@@ Lihua Li @@aut@@ Yao Yu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	331640910
id	DOAJ097769630
language_de	englisch
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callnumber-first	T - Technology
author	Xiaoyang Chen
spellingShingle	Xiaoyang Chen misc TP1-1185 misc layer house misc CO<sub<2</sub< concentration misc prediction misc GRU misc LSTM misc Chemical technology 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
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topic_title	TP1-1185 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 layer house CO<sub<2</sub< concentration prediction GRU LSTM
topic	misc TP1-1185 misc layer house misc CO<sub<2</sub< concentration misc prediction misc GRU misc LSTM misc Chemical technology
topic_unstemmed	misc TP1-1185 misc layer house misc CO<sub<2</sub< concentration misc prediction misc GRU misc LSTM misc Chemical technology
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title	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
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title_full	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
author_sort	Xiaoyang Chen
journal	Sensors
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author_browse	Xiaoyang Chen Lijia Yang Hao Xue Lihua Li Yao Yu
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doi_str_mv	10.3390/s24010244
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title_sort	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
callnumber	TP1-1185
title_auth	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
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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container_issue	1, p 244
title_short	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
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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

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