{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:16:19Z","timestamp":1785428179993,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003627","name":"Rural Development Administration","doi-asserted-by":"publisher","award":["PJ017484"],"award-info":[{"award-number":["PJ017484"]}],"id":[{"id":"10.13039\/501100003627","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PJ017484"],"award-info":[{"award-number":["PJ017484"]}],"id":[{"id":"https:\/\/ror.org\/03xs9yg50","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>The increasing frequency of heatwaves and high-temperature events due to climate change intensifies heat stress and mortality risk in broiler production and necessitates reassessment of facility design and energy management. This study developed a machine learning surrogate model trained on BES-simulated heating and cooling loads to estimate future energy load changes in broiler houses under SSP climate scenarios. Training data were constructed using simulated energy load results under different insulation conditions combined with historical meteorological data (2011\u20132020). Four machine learning models, namely Linear Regression, Random Forest, Gradient Boosting, and XGBoost, were applied to compare their predictive performance for heating and cooling loads. Model performance was evaluated using five-fold cross-validation (K-fold cross-validation, K = 5). XGBoost and Random Forest showed the best performance for heating and cooling load prediction, respectively (R2 &gt; 0.99, MAPE &lt; 10%). These models were applied to SSP1-2.6 and SSP5-8.5 climate scenarios. Results showed decreasing heating loads and increasing cooling loads across all scenarios, with cooling demand projected to increase by more than 150% relative to baseline levels under SSP5-8.5 by the late 21st century. This study demonstrates that simulation-trained machine learning can efficiently estimate long-term energy-demand changes, supporting climate-responsive facility design and energy management in livestock housing.<\/jats:p>","DOI":"10.3390\/ani16132097","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T16:20:39Z","timestamp":1783354839000},"page":"2097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BES-Driven Machine Learning Prediction of Future Energy Loads in Broiler Housing Under SSP Climate Scenarios in South Korea"],"prefix":"10.3390","volume":"16","author":[{"given":"Jaeeun","family":"Kim","sequence":"first","affiliation":[{"name":"Smart Animal Environment Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5656-3441","authenticated-orcid":false,"given":"Kyeong-Seok","family":"Kwon","sequence":"additional","affiliation":[{"name":"Research Policy Planning Division, Research Policy Bureau, Rural Development Administration, Jeonju 54875, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3494-5371","authenticated-orcid":false,"given":"Soon-kun","family":"Choi","sequence":"additional","affiliation":[{"name":"Planning and Coordination Bureau, Rural Development Administration, Jeonju 54875, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3089-2519","authenticated-orcid":false,"given":"Jong-Bok","family":"Kim","sequence":"additional","affiliation":[{"name":"Smart Animal Environment Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong-Hwa","family":"Jang","sequence":"additional","affiliation":[{"name":"Smart Animal Environment Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4651-6857","authenticated-orcid":false,"given":"Byeonghyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"Smart Animal Environment Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Smart Animal Environment Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability, Cambridge University Press."},{"key":"ref_2","unstructured":"MAFRA (2024). Key Statistics on Agriculture, Food and Rural Affairs."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"628","DOI":"10.3382\/japr.2005-00088","article-title":"Differential effects of heat stress in three strains of laying hens","volume":"16","author":"Scheideler","year":"2007","journal-title":"J. Appl. Poult. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.5536\/KJPS.2024.51.2.57","article-title":"Effects of pad cooling systems in tunnel-ventilated broiler house on reducing indoor temperature and temperature\u2013humidity index during summer","volume":"51","author":"Kim","year":"2024","journal-title":"Korean J. Poult. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110","DOI":"10.2141\/jpsa.42.110","article-title":"Progressive alteration to core temperature, respiration and blood acid\u2013base balance in broiler chickens exposed to acute heat stress","volume":"42","author":"Toyomizu","year":"2005","journal-title":"J. Poult. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.3382\/ps.2011-01951","article-title":"Interaction of hen production type, age, and temperature on laying pattern and egg quality","volume":"91","author":"Tumova","year":"2012","journal-title":"Poult. Sci."},{"key":"ref_7","first-page":"100082","article-title":"The impacts of climate change on livestock: An interdisciplinary scoping review of health, production, and adaptation strategies","volume":"2","author":"Anuta","year":"2025","journal-title":"Clim. Smart Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e1416","DOI":"10.1002\/vms3.1416","article-title":"Climate change and broiler production","volume":"10","author":"Oke","year":"2024","journal-title":"Vet. Med. Sci."},{"key":"ref_9","unstructured":"KMA (2025, September 25). Climate Statistics Analysis: Number of Heatwave Days. Available online: https:\/\/data.kma.go.kr\/climate\/heatWave\/selectHeatWaveMixChart.do?pgmNo=674."},{"key":"ref_10","unstructured":"MAFRA (2018). Livestock Mortality Due to Heatwaves."},{"key":"ref_11","unstructured":"KMA (2023). Regional Climate Change Projection Report for 17 Metropolitan Cities and Provinces (Revised Edition)."},{"key":"ref_12","unstructured":"NIMS (2020). Climate Change Projection Report over the Korean Peninsula 2020."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Costantino, A. (2023). Development, validation, and application of building energy simulation models for livestock houses: A systematic review. Agriculture, 13.","DOI":"10.3390\/agriculture13122280"},{"key":"ref_14","first-page":"535","article-title":"Energy loads calculation of standard design models for laying hens using a BES technique and development of regression model for energy loads prediction","volume":"24","author":"Kwon","year":"2023","journal-title":"J. Korea Acad.-Ind. Coop. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cho, J.-H., Lee, I.-B., Lee, S.-Y., Park, S.-J., Jeong, D.-Y., Decano-Valentin, C., Kim, J.-G., Choi, Y.-B., Jeong, H.-H., and Yeo, U.-H. (2022). Development of heat stress forecasting system in mechanically ventilated broiler house using dynamic energy simulation. Agriculture, 12.","DOI":"10.3390\/agriculture12101666"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127639","DOI":"10.1016\/j.jclepro.2021.127639","article-title":"Identification of energy-efficient solutions for broiler house envelopes through a primary energy approach","volume":"312","author":"Costantino","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_17","first-page":"456","article-title":"Evaluation of thermal stress of poultry according to stocking densities using numerical BES model","volume":"20","author":"Kwon","year":"2019","journal-title":"J. Korea Acad.-Ind. Coop. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.biosystemseng.2020.10.015","article-title":"Dynamic energy modelling for analysis of the thermal and hygroscopic environment in a mechanically ventilated duck house","volume":"200","author":"Lee","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104317","DOI":"10.1016\/j.biosystemseng.2025.104317","article-title":"Predicting energy needs in mechanically ventilated broiler houses in South Korea using a building energy simulation-driven algorithm","volume":"261","author":"Kwon","year":"2026","journal-title":"Biosyst. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ardabili, S., Abdolalizadeh, L., Mako, C., Torok, B., and Mosavi, A. (2022). Systematic review of deep learning and machine learning for building energy. Front. Energy Res., 10.","DOI":"10.3389\/fenrg.2022.786027"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116452","DOI":"10.1016\/j.apenergy.2021.116452","article-title":"A review of machine learning in building load prediction","volume":"285","author":"Zhang","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_22","unstructured":"(2019). Livestock House Design Standards (for Poultry and Duck), MAFRA & Nonghyup."},{"key":"ref_23","unstructured":"KOSIS (2025, September 25). Livestock Products Statistics. Available online: https:\/\/kosis.kr\/statHtml\/statHtml.do?orgId=101&tblId=DT_1EO097&conn_path=I2."},{"key":"ref_24","first-page":"75","article-title":"Estimation of THI index to evaluate thermal stress of animal-occupied zone in a broiler house using BES method","volume":"60","author":"Ha","year":"2018","journal-title":"J. Korean Soc. Agric. Eng."},{"key":"ref_25","unstructured":"IPCC (2021). Climate Change 2021: The Physical Science Basis, Cambridge University Press."},{"key":"ref_26","unstructured":"IPCC (2014). Evaluation of climate models. Climate Change 2013: The Physical Science Basis, Cambridge University Press."},{"key":"ref_27","unstructured":"KMA (2025, September 25). Climate Information Portal\u2014Meteorological Observation Data. Available online: https:\/\/www.climate.go.kr\/home\/."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s13143-019-00144-7","article-title":"Evaluation of the Korea Meteorological Administration Advanced Community Earth-System model (K-ACE)","volume":"56","author":"Lee","year":"2020","journal-title":"Asia-Pac. J. Atmos. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4513","DOI":"10.1029\/2019MS001739","article-title":"UKESM1: Description and evaluation of the U.K. Earth System Model","volume":"11","author":"Sellar","year":"2019","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11343","DOI":"10.1038\/s41598-025-96446-0","article-title":"Optimizing the multi-model ensemble of CMIP6 GCMs for climate simulation over Bangladesh","volume":"15","author":"Talukder","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_31","unstructured":"NAAS (2022). UNIDForm: Climate Scenario Utilization Platform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"118676","DOI":"10.1016\/j.energy.2020.118676","article-title":"A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour","volume":"212","author":"Li","year":"2020","journal-title":"Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"114683","DOI":"10.1016\/j.apenergy.2020.114683","article-title":"Building thermal load prediction through shallow machine learning and deep learning","volume":"263","author":"Wang","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_34","first-page":"152","article-title":"Development of a machine learning-based low-rise residential building energy consumption prediction model","volume":"15","author":"Yoo","year":"2021","journal-title":"J. Korean Inst. Archit. Sustain. Environ. Build. Syst."},{"key":"ref_35","unstructured":"MOLIT (2023). Energy Conservation Design Standards for Buildings."}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/13\/2097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T16:29:19Z","timestamp":1783355359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/13\/2097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,6]]},"references-count":35,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["ani16132097"],"URL":"https:\/\/doi.org\/10.3390\/ani16132097","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,6]]}}}