Analyzing Greenhouse Heating and Ventilation Scenarios for Temperature Setpoint Violations and Energy Usage by Means of an EnergyPlus Simulation

Yoel Kim; Juyeon Park; Kwangbok Jeong; Hyeonji Park; Nayoon Choi; Soo Hyun Park; Do Yeon Won; Hyun Kwon Suh

doi:10.7235/HORT.20250052

Online First

Research Article

Analyzing Greenhouse Heating and Ventilation Scenarios for Temperature Setpoint Violations and Energy Usage by Means of an EnergyPlus Simulation 에너지플러스 시뮬레이션을 통한 온실 난방 및 환기 시나리오의 온도 설정 기준 위반 및 에너지 소비 비교

PDF XML

Abstract

ICT-based horticultural greenhouses are recognized as a potential solution to major challenges in modern agriculture, including food crises, climate change, and declining rural populations. As the proportion of heated greenhouse area has increased to 36%, the heating demand in greenhouses is expected to continue to expand. However, heating oil and electricity are primarily used for greenhouse environment control, with heated greenhouses using these energy sources accounting for 90% of the total area. Recent unstable energy supplies and rising costs have significantly increased the financial pressure on greenhouse operations, making it critical to develop better energy management strategies. This study develops an energy simulation model using EnergyPlus to optimize climate control strategies in a demonstration greenhouse located in Pyeongchang, Gangwon Province, Korea. The simulated temperatures were compared with the measured greenhouse temperatures to ensure the accuracy of the simulation. The results showed that the Cv(RMSE) and MBE values for the monthly period were within 30% and ±10%, respectively. Over the entire period, the model maintained a Cv(RMSE) outcome of 19.38% and MBE rate of 0.52%, showing acceptable margins of error between simulated and measured temperature values. Three different scenarios were simulated based on heating and ventilation setpoint temperatures, and the deviation of the simulated temperature from the setpoint range was quantitatively evaluated. In addition, electricity and oil consumption for heating were compared among the scenarios. The total simulated energy loads were 195,428 MJ, 255,913 MJ, and 223,608 MJ for Scenarios 1, 2, and 3, respectively, with Scenario 2 consuming the most energy. Similarly, oil consumption for heating was highest in Scenario 2 at 5,721 L, while electricity consumption was similar in all three scenarios at 12,773 kWh, 12,926 kWh, and 12,826 kWh, respectively. This study demonstrates that a building energy simulation model can effective for the prediction and analysis of energy consumption in a greenhouse. Further studies are needed to validate the correspondence more thoroughly between simulated and actual energy consumption in greenhouse operations. Furthermore, plant growth models need to be integrated into the simulation framework to more accurately simulate internal greenhouse conditions and better analyze energy consumption. This study will contribute to the establishment of optimal control strategies based on artificial intelligence for greenhouse environments.

Keywords

building energy simulations

decision support system

energy modeling

greenhouse climate control

smart agriculture

ICT 기반의 원예 온실은 식량 위기, 기후 변화, 농촌 인구 감소 등 현대 농업의 주요 과제에 대한 잠재적 해결방안으로 주목받고 있다. 채소 시설원예의 가온 온실 면적 비중이 36%까지 증가하면서 온실 난방 수요도 증가할 전망이다. 그러나 온실 환경 제어에는 주로 석유와 전기가 사용되며, 이들 에너지원을 사용하는 가온 온실 면적이 전체의 90%를 차지한다. 최근 불안정한 에너지 공급과 비용 상승으로 인해 온실 운영 비용의 부담이 증가하고 있으며, 이에 따라 효율적인 에너지 관리 방안이 요구되고 있다. 본 연구는 강원도 평창에 위치한 실증 온실을 대상으로 환경 제어 전략을 최적화하기 위해 EnergyPlus를 사용하여 에너지 시뮬레이션 모델을 개발하였다. 시뮬레이션으로 도출된 온도를 실제 온실의 온도 데이터와 비교하여 시뮬레이션 모델의 신뢰성을 확인하였다. 그 결과 월별 Cv(RMSE)와 MBE 값이 각각 30% 및 ±10% 이내로 나타났다. 전체 기간에 대해서도 모델은 Cv(RMSE) 19.38%의 오차와 MBE 0.52%를 유지하여 시뮬레이션으로 도출된 온도와 실제 온실 온도간의 오차가 허용 범위 내에 있음을 확인하였다. 난방 및 환기 설정 온도값에 기반한 세 가지 시나리오를 적용하여, 시뮬레이션을 통해 산출된 온도가 설정된 온도 범위를 얼마나 벗어났는지를 정량적으로 평가하였다. 또한, 시나리오별 전력 및 난방용 등유 사용량을 비교하였다. 시뮬레이션으로 도출된 총 에너지 부하는 Scenario 1, 2, 3에서 각각 195,428MJ, 255,913MJ, 223,608MJ로 나타났으며, Scenario 2에서 가장 많은 에너지가 소비되었다. 등유 사용량 역시 Scenario 2가 5,721L로 가장 많았으며, 전력 사용량은 세 시나리오 모두 유사하여 각각 12,773kWh, 12,926kWh, 12,826kWh로 나타났다. 본 연구는 건물 에너지 시뮬레이션 모델이 온실 에너지 소비 예측 및 분석에 효과적으로 활용될 수 있음을 보여주었다. 향후 연구에서는 시뮬레이션에서 산출된 에너지 사용량이 실제 온실 운영 데이터와 얼마나 일치하는지 검증하는 과정이 필요하다. 또한, 작물 생육 모델을 시뮬레이션 프레임워크에 통합하여 온실 내부 환경을 보다 정밀하게 시뮬레이션하고 에너지 소비를 보다 정확하게 분석함이 필요하다. 본 연구 결과는 향후 인공지능 기반의 온실 최적 제어 전략을 수립하는 데 기여할 것으로 기대된다.

키워드

건물 에너지 시뮬레이션

의사결정 지원 시스템

에너지 모델링

온실 기후 조절

스마트 농업

References

Adesanya MA, Na WH, Rabiu A, Ogunlowo QO, Akpenpuun TD, Rasheed A, Yoon YC, Lee HW (2022) TRNSYS Simulation and experimental validation of internal temperature and heating demand in a glass greenhouse. Sustainability 14:8283. https://doi.org/10.3390/su14148283

10.3390/su14148283

Ahamed MS, Guo H, Tanino K (2019) Energy saving techniques for reducing the heating cost of conventional greenhouses. Biosyst Eng 178:9-33. https://doi.org/10.1016/J.BIOSYSTEMSENG.2018.10.017

10.1016/j.biosystemseng.2018.10.017

Ajagekar A, Mattson NS, You F (2023) Energy-efficient AI-based control of semi-closed greenhouses leveraging robust optimization in deep reinforcement learning. Adv Appl Energy 9:100-119. https://doi.org/10.1016/J.ADAPEN.2022.100119

10.1016/j.adapen.2022.100119

An Z, Cao X, Yao Y, Zhang W, Li L, Wang Y, Guo S, Luo D (2021) A Simulator-based planning framework for optimizing autonomous greenhouse control strategy. ICAPS 31:436-444. https://doi.org/10.1609/ICAPS.V31I1.15989

10.1609/icaps.v31i1.15989

Beaulac A, Lalonde T, Haillot D, Monfet D (2024) Energy modeling, calibration, and validation of a small-scale greenhouse using TRNSYS. Appl Therm Eng 248:123-195. https://doi.org/10.1016/J.APPLTHERMALENG.2024.123195

10.1016/j.applthermaleng.2024.123195

Boccalatte A, Fossa M, Sacile R (2021) Modeling, Design and Construction of a Zero-Energy PV Greenhouse for Applications in Mediterranean Climates. Therm Sci Eng Prog 25:101046. https://doi.org/10.1016/J.TSEP.2021.101046

10.1016/j.tsep.2021.101046

Bre F, Machado RM, Lawrie LK, Crawley DB, Lamberts R (2021) Assessment of solar radiation data quality in typical meteorological years and its influence on the building performance simulation. Energy Build 250:111-251. https://doi.org/10.1016/j.enbuild.2021.111251

10.1016/j.enbuild.2021.111251

Castro RP, Silva PD, Pires LCC (2024) Advances in Solutions to Improve the Energy Performance of Agricultural Greenhouses: A Comprehensive Review. Appl Sci 14:6158. https://doi.org/10.3390/APP14146158

10.3390/app14146158

Chahidi LO, Fossa M, Priarone A, Mechaqrane A (2021) Greenhouse cultivation in Mediterranean climate: Dynamic energy analysis and experimental validation. Therm Sci Eng Prog 26:101-102. https://doi.org/10.1016/J.TSEP.2021.101102

10.1016/j.tsep.2021.101102

Chen L, Xu L, Wei R (2023) Energy-Saving Control Algorithm of Venlo Greenhouse Skylight and Wet Curtain Fan Based on Reinforcement Learning with Soft Action Mask. Agriculture 13:141, https://doi.org/10.3390/AGRICULTURE13010141

10.3390/agriculture13010141

Choab N, Allouhi A, Maakoul A El, Kousksou T, Saadeddine S, Jamil A (2021) Effect of greenhouse design parameters on the heating and cooling requirement of greenhouses in Moroccan climatic conditions. IEEE Access 9:2986-3003. https://doi.org/10.1109/ACCESS.2020.3047851

10.1109/ACCESS.2020.3047851

Elings A, Kempkes FLK, Kaarsemaker RC, Ruijs MNA, Van De Braak NJ, Dueck TA (2005) The energy balance and energy-saving measures in greenhouse tomato cultivation. Acta Hortic 691:67-74. https://doi.org/10.17660/ACTAHORTIC.2005.691.5

10.17660/ActaHortic.2005.691.5

Ge Q, Ke Z, Liu Y, Chai F, Yang W, Zhang Z, Wang Y (2023) Low-carbon strategy of demand-based regulating heating and lighting for the heterogeneous environment in Beijing Venlo-type greenhouse. Energy 267:126513. https://doi.org/10.1016/j.energy.2022.126513

10.1016/j.energy.2022.126513

Gercek M, Durmuş Arsan Z (2019) Energy and environmental performance based decision support process for early design stages of residential buildings under climate change. Sustain Cities Soc 48:101-580. https://doi.org/10.1016/J.SCS.2019.101580

10.1016/j.scs.2019.101580

Goo J, Shin H, Kwak Y, Park DY, Huh JH (2024) Development of an applicability and performance evaluation tool based on energy simulation for smart greenhouse cooling packages. Appl Therm Eng 240:122240. https://doi.org/10.1016/j.applthermaleng.2023.122240

10.1016/j.applthermaleng.2023.122240

Greenhouse Vegetable Production Status (2023) Korean Statistical Information Service (KOSIS). https://kosis.kr/statHtml/statHtml.do?orgId=114&tblId=DT_114018_019&conn_path=I2. Accessed 19 March 2025

Hashan MM, Hasan FKM, Khandaker FRM, Karmaker CK, Deng Z, Zilani JM (2017) Functional properties improvement of socks items using different types of yarn. Int J Text Sci 2017:34-42. https://doi.org/10.5923/j.textile.20170602.02

10.5923/j.textile.20170602.02

He K, Chen D, Sun L, Huang Z, Liu Z (2014) Analysis of the climate inside multi-span plastic greenhouses under different shade strategies and wind regimes. Hortic Sci Technol 32:473-483. https://doi.org/10.7235/hort.2014.13156

10.7235/hort.2014.13156

Hou Z, Wang Z, Sun Z, Gao P, Hu Y, Fan J (2024) Annual energy performance simulation of ground source heat pump in a solar greenhouse. Case Stud Therm Eng 59:104-556. https://doi.org/10.1016/J.CSITE.2024.104556

10.1016/j.csite.2024.104556

Hu G, You F (2022) Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management. Renew Sustain Energy Rev 168:112-790. https://doi.org/10.1016/J.RSER.2022.112790

10.1016/j.rser.2022.112790

Jung DH, Kim HJ, Kim JY, Lee TS, Park SH (2020) Design optimization of proportional plus derivative band parameters used in greenhouse ventilation by response surface methodology. Hortic Sci Technol 38:187-200. https://doi.org/10.7235/HORT.20200018

10.7235/HORT.20200018

Kim H, Kim D (2022) Gi-hu-hwan-gyeong-eul wi-han do-si nong-eob-ui sin-jae-saeng e-neo-ji bi-gyo: hwa-hwe-wa chae-so-leul jung-sim-eu-lo [Renewable energy comparison of urban agriculture for climatic environment: focus on flowers and vegetables]. The Seoul Institute 22-13. https://www.si.re.kr/sites/default/files/small%2022-13.pdf [In Korean]

Kim SY, Lee SM, Park KS, Ryu KH (2018) Prediction model of internal temperature using backpropagation algorithm for climate control in greenhouse. Hortic Sci Technol 36:713-729. https://doi.org/10.12972/KJHST.20180071

10.12972/kjhst.20180071

Lawrie LK, Crawley DB (2019) Development of global typical meteorological years (TMYx). Available via https://Climate.Onebuilding.Org Accessed 9 August 2023

Lee SH, Kim RW, Kim CM, Seok HW, Yoon S (2023) Optimal capacity determination of hydrogen fuel cell technology based trigeneration system and prediction of semi-closed greenhouse dynamic energy loads using building energy simulation. J Bio-Env Con 32:181-189. https://doi.org/10.12791/KSBEC.2023.32.3.181

10.12791/KSBEC.2023.32.3.181

Moretti E, Zinzi M, Carnielo E, Merli F (2017) Advanced polycarbonate transparent systems with aerogel: Preliminary Characterization of Optical and Thermal Properties. Energy Procedia 113:9-16. https://doi.org/10.1016/j.egypro.2017.04.003

10.1016/j.egypro.2017.04.003

Omer AM (2024) Greenhouse gardening for food production and the environment. Int J Hortic Food Sci 6:01-20. https://doi.org/10.33545/26631067.2024.V6.I1A.179

10.33545/26631067.2024.v6.i1a.179

Pan Y, Zhu M, Lv Y, Yang Y, Liang Y, Yin R, Yang Y, Jia X, Wang X, et al. (2023) Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies. Adv Appl Energy 10:100-135. https://doi.org/10.1016/J.ADAPEN.2023.100135

10.1016/j.adapen.2023.100135

Rasheed A, Kwak CS, Na WH, Lee JW, Kim HT, Lee HW (2020) Development of a building energy simulation model for control of multi-Span greenhouse microclimate. Agronomy 10:1236. https://doi.org/10.3390/AGRONOMY10091236

10.3390/agronomy10091236

Taki M, Rohani A, Rahmati-Joneidabad M (2018) Solar thermal simulation and applications in greenhouse. Inf Process Agric 5:83-113. https://doi.org/10.1016/J.INPA.2017.10.003

10.1016/j.inpa.2017.10.003

Tam C, Zhao Y, Liao Z, Zhao L (2020) Mitigation strategies for overheating and high carbon dioxide concentration within institutional buildings: A case study in Toronto, Canada. Buildings 10:124. https://doi.org/10.3390/BUILDINGS10070124

10.3390/buildings10070124

Wang H, Lu N, Wu F, Zhai J (2023) Coupling Computational Fluid Dynamics and EnergyPlus to Optimize Energy Consumption and Comfort in Air Column Ventilation at a Tall High-Speed Rail Station. Sustainability 15:12948. https://doi.org/10.3390/SU151712948

10.3390/su151712948

Information

Publisher :KOREAN SOCIETY FOR HORTICULTURAL SCIENCE
Publisher(Ko) :한국원예학회
Journal Title :Horticultural Science and Technology
Journal Title(Ko) :원예과학기술지
Received Date : 2025-02-07
Revised Date : 2025-05-14
Accepted Date : 2025-05-16
DOI :https://doi.org/10.7235/HORT.20250052

Horticultural Science and Technology 원예과학기술지 ISSN:1226-8763(Print) 2465-8588(Online)

Online First