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) |
1 | Izar-Tenorio, J., P. Jaramillo, W. M. Griffin, and M. Small, 2020. Impacts of projected climate change scenarios on heating and cooling demand for industrial broiler chicken farming in the Eastern US. Journal of Cleaner Production 255: 120306. doi:10.1016/j.jclepro.2020.120306. DOI |
2 | Kim R. W., S.-W. Hong, T. Norton, T. Amon, A. Youssef, D. Berckmans, and I.-B. Lee, 2020. Computational fluid dynamics for non-experts: development of a user-friendly CFD simulator (HNVR-SYS) for natural ventilation design applications. Biosystems Engineering 193: 232-246. doi:10.1016/j.biosystemseng.2020.03.005. DOI |
3 | Kim, S.-C., 2017. 4th industrial revolution and smart farm technology. Rural Resources, Magazine of the Korean Society of Agricultural Engineers 59(2): 10-18. (in Korean). |
4 | Garcia, R., J. Aguilar, M. Toro, A. Pinto, and P. Rodriguez, 2020. A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture 179: 105826. doi:10.1016/j.compag.2020.105826. DOI |
5 | Hong, S.-W. and I. B. Lee, 2014. Predictive model of micro-environment in a naturally ventilated greenhousec for a model-based control approach. Protected Horticulture and Plant Factory 23(3): 181-191. (in Korean). doi:10.12791/KSBEC.2014.23.3.181. DOI |
6 | Kim R.-W., J.-G. Kim, I.-B. Lee, U.-H. Yeo, and S.-Y. Lee, 2019. Development of a VR simulator for educating CFD-computed internal environment of piglet house. Biosystems Engineering 188: 243-264. doi:10.1016/j.biosystemseng.2019.10.024. DOI |
7 | Kwon, K.-S., 2017. R&D and current state of livestock smart farm using ICT converence technology. Rural Resources, Magazine of the Korean Society of Agricultural Engineers 59(2): 38-45. (in Korean). |
8 | Tong, X., S.-W. Hong, L. Zhao, 2019. CFD modeling of airflow, thermal environment and ammonia concentration distribution in a commercial manure-belt layer house with mixed ventilation systems. Computers and Electronics in Agriculture 162: 281-299. doi:10.1016/j.compag.2019.03.031. DOI |
9 | Li, J., V. Narayanan, E. Kebreab, S. Dikmen, and J. G. Fadel, 2021. A mechanistic thermal balance model of dairy cattle. Biosystems engineering 209: 256-270. doi:10.1016/j.biosystemseng.2021.06.009. DOI |
10 | Sakomura, N. K., F. A. Longo, E. O. Oviedo-Rondon, C. Boa-Viagem, and A. Ferraudo, 2005. Modeling energy utilization and growth parameter description for broiler chickens. Poultry Science 84(9): 1363-1369. doi:10.1093/ps/84.9.1363. DOI |
11 | Tullo, E., G. Aletti, A. Micheletti, G. Naldi, A. P. Fernandez, E. Vranken, D. Berckmans, and M. Guarino, 2018. The influence of microclimate on the development of foot pad dermatitis in broilers. 10th International Livestock Environment Symposium (ILES X), p. 1. American Society of Agricultural and Biological Engineers. doi:10.13031/iles.18-090. DOI |
12 | Yeo, U.-H., I.-B. Lee, K.-S. Kwon, T. Ha, S.-H. Park, R.-W. Kim, and S.-Y. Lee, 2016. Analysis of research trend and core technologies based on ICT to materialize smart-farm. Protected Horticulture and Plant Factory 25(1): 30-41. (in Korean). doi:10.12791/KSBEC.2016.25.1.30. DOI |
13 | Yoo, J.-E. 2015. Random forests, an alternative data mining technique to decision tree. Journal of Educational Evaluation 28(2): 427-448. (in Korean). |
14 | NIAS, 2022. Broiler breeding management - Method of determining the breeding density of broilers. National Institute of Animal Science, Accessed at https://www.nongsaro.go.kr/portal/ps/psb/psbk/kidoContentsFileView.ps?ep=SRafvgcz7ZIqtMkSj13MnYDhcJ@8SniWJoCSUU7jI@E! |
15 | Kucuktopcu, E., B. Cemek, H. Simsek, and J. Q. Ni, 2022. Computational fluid dynamics modeling of a broiler house microclimate in summer and winter. Animals 12(7): 867. doi:10.3390/ani12070867. DOI |
16 | Lee, I.-B., J.P. Bitog, S.-W. Hong, I.-H. Seo, K.-S. Kwon, T. Bartzanas, and M. Kacira, 2013. The past, present and future of CFD for agro-environental applications. Computers and Electronics in Agriculture 93: 168-183. doi:j.compag.2012.09.006. DOI |
17 | Lee, S.-Y., I.-B. Lee, U.-H. Yeo, J.-G. Kim, and R.-W. Kim, 2022. Machine learning approach to predict air temperature and relative humidity inside mechanically and naturally ventilated duck houses: application of recurrent neural network. Agriculture 12(3): 318. doi:10.3390/agriculture12030318. DOI |
18 | Pedersen, S., 2015. The influence of diurnal variation in animal activity and digestion on animal heat production. Agricultural Engineering International: CIGR Journal. Special Issue: 18th World Congress of CIGR. Accessed at https://cigrjournal.org/index.php/Ejounral/article/view/3097. |
19 | Xie, Q., J. Q. Ni, J. Bao, and Z. Su, 2019. A thermal environmental model for indoor air temperature prediction and energy consumption in pig building. Building and Environment 161: 106238. doi:10.1016/j.buildenv.2019.106238. DOI |
20 | Sharma, I., Canizares, C., and Bhattacharaya, K. 2015. Residential micro-hub load model using network. 2015 North American Power Symposium(NAPS), 1-6. doi:10.1109/NAPS.2015.7335091. DOI |
21 | Youssef, A., H. H. Yen, S. E. Ozc an, and D. Berc kmans, 2011. Data-based mechanistic modelling of indoor temperature distributions based on energy input. Energy and Buildings 43(11): 2965-2972. doi:10.1016/j.enbuild.2011.06.042. DOI |
22 | Arulmozhi, E., J. K. Basak, T. Sihalath, J. Park, H. T. Kim, and B. E. Moon, 2021. Machine learning-based microclimate model for indoor air temperature and relative humidity prediction in a swine building. Animals 11(1): 222. doi:10.3390/ani11010222. DOI |
23 | Choi, Y.-J., B.-R. Park, J.-H. Cho, J.-W. Moon, 2020. Development of Supply Air Temperature Prediction Model for Optimal Control Algorithm of Containment Data Center. Korea Institute of Ecological Architecture and Environme 20(5): 159-164. (in Korean). doi:10.12813/kieae.2020.20.5.159. DOI |
24 | CIGR, 2002. Heat and Moisture Production at Animal and House Levels. CIGR 4th Report of Working Group on Climatization of Animal. International Commission of Agricultural Engineering (CIGR), Section II. Accessed at https://www.cigr.org/sites/default/files/documets/CIGR_4TH_WORK_GR.pdf |
25 | Haupt, R.L., and S.E. Haupt, 2004. Practical genetic algorithms, second edition. Wiley-Interscience, New Jersey, USA: 215-219. |
26 | Hong, S.-W., A. K. Moon, Song, L., and I. B. Lee, 2015. Data-based model approach to predict internal air temperature of greenhouse. Journal of the Korean Society of Agricultural Engineers 57(3): 9-19. (in Korean). doi: 10.5389/KSAE.2015.57.3.009. DOI |
![]() |