A Hybrid Medium and Long-Term Relative Humidity Point and Interval Prediction Method for Intensive Poultry Farming

Hang Yin (), Zeyu Wu, Junchao Wu, Junjie Jiang, Yalin Chen, Mingxuan Chen, Shixuan Luo and Lijun Gao ()
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Hang Yin: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Zeyu Wu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Junchao Wu: Institute of Collaborative Innovation, University of Macau, Macao 999078, China
Junjie Jiang: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Yalin Chen: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Mingxuan Chen: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Shixuan Luo: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Lijun Gao: College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China

Mathematics, 2023, vol. 11, issue 14, 1-22

Abstract: The accurate and reliable relative humidity (RH) prediction holds immense significance in effectively controlling the breeding cycle health and optimizing egg production performance in intensive poultry farming environments. However, current RH prediction research mainly focuses on short-term point predictions, which cannot meet the demand for accurate RH control in poultry houses in intensive farming. To compensate for this deficiency, a hybrid medium and long-term RH prediction model capable of precise point and interval prediction is proposed in this study. Firstly, the complexity of RH is reduced using a data denoising method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and permutation entropy. Secondly, important environmental factors are selected from feature correlation and change trends. Thirdly, based on the results of data denoising and feature selection, a BiGRU-Attention model incorporating an attention mechanism is established for medium and long-term RH point prediction. Finally, the Gaussian kernel density estimation (KDE-Gaussian) method is used to fit the point prediction error, and the RH prediction interval at different confidence levels is estimated. This method was applied to analyze the actual collection of waterfowl (Magang geese) environmental datasets from October 2022 to March 2023. The results indicate that the CEEMDAN-FS-BiGRU-Attention model proposed in this study has excellent medium and long-term point prediction performance. In comparison to LSTM, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are reduced by 57.7%, 48.2%, and 56.6%, respectively. Furthermore, at different confidence levels, the prediction interval formed by the KDE-Gaussian method is reliable and stable, which meets the need for accurate RH control in intensive farming environments.

Keywords: medium and long-term point prediction; interval prediction; data denoising; feature selection; BiGRU; attention mechanism; KDE-Gaussian (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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