A Hybrid Medium and Long-Term Relative Humidity Point and Interval Prediction Method for Intensive Poultry Farming
نویسندگان
چکیده
The accurate and reliable relative humidity (RH) prediction holds immense significance in effectively controlling the breeding cycle health optimizing egg production performance intensive poultry farming environments. However, current RH research mainly focuses on short-term point predictions, which cannot meet demand for control houses farming. To compensate this deficiency, a hybrid medium long-term model capable of precise interval is proposed study. Firstly, complexity reduced using data denoising method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) permutation entropy. Secondly, important environmental factors are selected from feature correlation change trends. Thirdly, based results selection, BiGRU-Attention incorporating an attention mechanism established prediction. Finally, Gaussian kernel density estimation (KDE-Gaussian) used to fit error, at different confidence levels estimated. This was applied analyze actual collection waterfowl (Magang geese) datasets October 2022 March 2023. indicate CEEMDAN-FS-BiGRU-Attention study has excellent performance. In comparison LSTM, mean absolute error (MAE), root square (RMSE), percentage (MAPE) by 57.7%, 48.2%, 56.6%, respectively. Furthermore, levels, formed KDE-Gaussian stable, meets need
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ژورنال
عنوان ژورنال: Mathematics
سال: 2023
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math11143247