데이터 기반 모델에 의한 강제환기식 육계사 내 기온 변화 예측
Data-Based Model Approach to Predict Internal Air Temperature in a Mechanically-Ventilated Broiler House
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The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimatecontrol can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better controlof indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal airtemperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climateand ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. Theresults indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracywith the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematicaldescriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-boxmachine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.
Abstract
The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimatecontrol can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better controlof indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal airtemperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climateand ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. Theresults indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracywith the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematicaldescriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-boxmachine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.
참고문헌 (26)
- Arulmozhi, E., "Machine learning-based microclimate model for indoor air temperature and relative humidity prediction in a swine building", Animals, 11(1), pp.222~ (2021)[DOI]
- 최영재, "컨테인먼트형 데이터센터 최적 제어 알고리즘을 위한 열환경 예측모델 개발", KIEAE Journal, 20(5), pp.159~164 (2020), 한국생태환경건축학회[DOI]
- Heat and Moisture Production at Animal and House Levels. CIGR 4th Report of Working Group on Climatization of Animal, Section II, "CIGR", International Commission of Agricultural Engineering (CIGR), ~ (2002)
- Garcia, R., "A systematic literature review on the use of machine learning in precision livestock farming", Computers and Electronics in Agriculture, 179, pp.105826~ (2020)[DOI]
- Haupt, R. L., "Practical genetic algorithms" (2004), Wiley-Interscience
- 홍세운, "데이터 기반 모델에 의한 온실 내 기온 변화 예측", 한국농공학회논문집, 57(3), pp.9~19 (2015), 한국농공학회
- 홍세운, "자연 환기식 온실의 모델 기반 환기 제어를 위한 미기상 환경 예측 모형", 생물환경조절학회지, 23(3), pp.181~191 (2014), (사) 한국생물환경조절학회
- Izar-Tenorio, J., "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, pp.120306~ (2020)[DOI]
- Kim R. W., "Computational fluid dynamics for non-experts : development of a user-friendly CFD simulator(HNVR-SYS)for natural ventilation design applications", Biosystems Engineering, 193, pp.232~246 (2020)[DOI]
- Kim R. -W., "Development of a VR simulator for educating CFD-computed internal environment of piglet house", Biosystems Engineering, 188, pp.243~264 (2019)[DOI]
- Kim, S. -C., "4th industrial revolution and smart farm technology", Rural Resources, Magazine of the Korean Society of Agricultural Engineers, 59(2), pp.10~18 (2017)
- Küçüktopcu, E., "Computational fluid dynamics modeling of a broiler house microclimate in summer and winter", Animals, 12(7), pp.867~ (2022)[DOI]
- Kwon, K. -S., "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), pp.38~45 (2017)
- Lee, I. -B., "The past, present and future of CFD for agro-environental applications", Computers and Electronics in Agriculture, 93, pp.168~183 (2013)
- Lee, S. -Y., "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), pp.318~ (2022)[DOI]
- Li, J., "A mechanistic thermal balance model of dairy cattle", Biosystems engineering, 209, pp.256~270 (2021)[DOI]
- "Broiler breeding management - Method of determining the breeding density of broilers" (2022)[URL]
- Pedersen, S., "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) (2015)
- Sakomura, N. K., "Modeling energy utilization and growth parameter description for broiler chickens", Poultry Science, 84(9), pp.1363~1369 (2005)[DOI]
- Sharma, I., "Residential micro-hub load model using network", 2015 North American Power Symposium(NAPS), pp.1~16 (2015)
- Tong, X., "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, pp.281~299 (2019)[DOI]
- Tullo, E., "The influence of microclimate on the development of foot pad dermatitis in broilers", 10th International Livestock Environment Symposium (ILES X), pp.1~ (2018), American Society of Agricultural and Biological Engineers
- Xie, Q., "A thermal environmental model for indoor air temperature prediction and energy consumption in pig building", Building and Environment, 161, pp.106238~ (2019)[DOI]
- 여욱현, "스마트팜 구현을 위한 연구동향 및 ICT 핵심기술 분석", 생물환경조절학회지, 25(1), pp.30~41 (2016), (사) 한국생물환경조절학회
- 유진은, "랜덤 포레스트: 의사결정나무의 대안으로서의 데이터 마이닝 기법", 교육평가연구, 28(2), pp.427~448 (2015), 한국교육평가학회
- Youssef, A., "Data-based mechanistic modelling of indoor temperature distributions based on energy input", Energy and Buildings, 43(11), pp.2965~2972 (2011)[DOI]