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http://dx.doi.org/10.5389/KSAE.2022.64.5.027

Data-Based Model Approach to Predict Internal Air Temperature in a Mechanically-Ventilated Broiler House  

Choi, Lak-yeong (Department of Rural and Bio-systems Engineering, Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture (BK21 four), Chonnam National University)
Chae, Yeonghyun (Department of Rural and Bio-systems Engineering, Chonnam National University)
Lee, Se-yeon (Department of Rural and Bio-systems Engineering, Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture (BK21 four), Chonnam National University)
Park, Jinseon (AgriBio Institute of Climate Change Management, Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture (BK21 four), Chonnam National University)
Hong, Se-woon (Department of Rural and Bio-systems Engineering, AgriBio Institute of Climate Change Management, Education and Research Unit for Climate-Smart Reclaimed-Tideland Agriculture (BK21 four), Chonnam National University)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.64, no.5, 2022 , pp. 27-39 More about this Journal
Abstract
The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.
Keywords
Genetic algorithm; machine learning; mechanistic model; smart farm;
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