https://doi.org/10.4081/jae.2022.1420
Effect of envelope characteristics on the accuracy of discretised greenhouse model in TRNSYS
Published: 7 July 2022
Abstract Views: 1141
Appendix: 195
PDF: 378
HTML: 61
PDF: 378
HTML: 61
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
TRNSYS is a standard tool recently used to model and simulate greenhouse energy demand and utilisation using building energy simulation (BES). Previously, a single thermal point was used for validation, ignoring the distribution of greenhouse climate parameters, especially the temperature. Temperature variation often leads to thermal stratification, prompting researchers to propose volume discretisation in dynamic greenhouse simulations. In this context, the effect of envelope characterisation on the accuracy of the discretised TRNSYS BES model was developed to determine the best BES model under a free-floating regime. The combination of the number of layers [double (D) and single (S)], geometry mode [3D and manual (M)], and layer type [massless (M) and no glazing window (W)], led to the development of five models: D_3D_M, D_3D_W, D_M_M, S_3D_W, and S_M_M. The simulation was performed in a standard radiation mode, and the output parameters were temperature and relative humidity (RH). R2 and the root square mean error (RSME) were used to check the fitness and degree of deviation, respectively, to validate the models. Analysis of variance (ANOVA) was employed to investigate the significant differences among the models, whereas contour plots were used to compare the distribution pattern between the significant models and experimental data. Validation of the models showed that the obtained R2 values ranged from 0.86 to 0.95, and the RSME values for the temperature were between 2.64°C and 3.91°C. These values were 0.91-0.93 and 19.72%-30.32% for RH. The ANOVA (P<0.05) result exhibited significant differences between the S-scenario models and experimental central points in temperature and RH. However, the D- and S-layer scenarios with a 3D geometry and massless layer showed similar distribution with their corresponding experimental greenhouses. Hence, 3D_M was regarded as the best combination in the discretised BES model.
Akpenpuun T. D., Na W.H., Ogunlowo Q.O., Rabiu A., Adesanya M.A., Addae K.S., Kim T.H., Lee H.W. 2021. Effect of Greenhouse Cladding Materials and Thermal Screen Configuration on Heating Energy and Strawberry ( Fragaria Ananassa Var. ‘Seolhyang’) Yield in Winter. Agron. 11(2498):1–23.
DOI: https://doi.org/10.3390/agronomy11122498
Akpenpuun T. D., Na W.H., Ogunlowo Q.O., Rabiu A., Adesanya M.A., Addae K.S., Kim T.H., Lee H.W. 2021. Effect of Glazing Configuration as an Energy-Saving Strategy in Naturally Ventilated Greenhouses for Strawberry ( Seolhyang Sp. ) Cultivation. J Agr Eng 52(2):1–24.
DOI: https://doi.org/10.4081/jae.2021.1177
Akpenpuun T. D., Ogunlowo Q. O., Rabiu A., Adesanya M. A., Na W. H., Omobowale M. O., Mijinyawa Y., and Lee H. W. 2022. Building Energy Simulation model application to greenhouse microclimate, covering material and thermal blanket modelling: A Review. Niger J Techn Dev. 19(3): 3851-3856.
Asa’d O., Ugursal V. I., Ben-Abdallah N. 2019. Investigation of the Energetic Performance of an Attached Solar Greenhouse through Monitoring and Simulation. Energ Sustain Dev 53:15–29.
DOI: https://doi.org/10.1016/j.esd.2019.09.001
Baglivo C., Mazzeo D, Panico S., Bonuso S., Matera N., Congedo P.M., Oliveti G. 2020. Complete Greenhouse Dynamic Simulation Tool to Assess the Crop Thermal Well-Being and Energy Needs. Appl Therm Eng 179:115698.
DOI: https://doi.org/10.1016/j.applthermaleng.2020.115698
Bello-robles J. C., Ruiz-leon J., Begovich O., Ruiz J., Quetziquel R. 2018. Modeling of the Temperature Distribution of a Greenhouse Using Finite Element Differential Neural Networks. Kybernetika. 54 (5): 1033–1048.
DOI: https://doi.org/10.14736/kyb-2018-5-1033
Blachowski W. 2021. A Guide to Model Calibration. Wunderman Thompson Technology Blog. Available from: https://wttech.blog/blog/2021/a-guide-to-model-calibration/
Bojacá, C. R., Gil R., Gómez S., Cooman A., Schrevens E. 2009. Analysis of Greenhouse Air Temperature Distribution Using Geostatistical Methods. T ASABE. 52(3): 957-968
DOI: https://doi.org/10.13031/2013.27393
Boulard, T., Fatnassi H., Majdoubi H., Bouirden L. 2008. Airflow and Microclimate Patterns in a One-Hectare Canary Type Greenhouse: An Experimental and CFD Assisted Study. Acta Hortic 801 PART 2(6):837–45.
DOI: https://doi.org/10.17660/ActaHortic.2008.801.98
Cesar, T. Q. Z., Leal P.A.M., Branquinho O.C., Felipe A.M.M. 2021. Wireless Sensor Network to Identify the Reduction of Meteorological Gradients in Greenhouse in Subtropical Conditions. J Agr Eng 52(1):1–8.
Choab, N., Allouhi A., El Maakoul A., 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.
DOI: https://doi.org/10.1109/ACCESS.2020.3047851
Coastalwiki. 2020. Definition of Model Calibration: Model Calibration. Available from: http://www.coastalwiki.org/wiki/Model_calibration#:~:text=Model calibration is the process,-Fit or Cost Function
Glen S. 2019. Comparing Model Evaluation Techniques Part 1: Statistical Tools & Tests. Data Science Central. Available from: https://www.datasciencecentral.com/comparing-model-evaluation-techniques/
Gupta R. 2019. An Introduction to Discretization Techniques for Data Scientists. Towards Data Science. Available from: https://towardsdatascience.com/an-introduction-to-discretization-in-data-science-55ef8c9775a2
Hamad, H., Al-smadi A., Ijjeh A.. 2008. Graphical Model Validation Methods For Analog And Mixed- Signal Electronic Circuits Design. Proc. Inter Conf. Micoelectronics. 353–56.
DOI: https://doi.org/10.1109/ICM.2008.5393849
Klein S. A. 2012. Trnsys, a Transient System Simulation Program; Solar Energy Laboratory, . Madison, WI, USA: University of Wisconsin-Madison.
Laghmich N., Ramoni Z., Lapisa R. Draoui A. 2022. Numerical Analysis of Horizontal Temperature Distribution in Large Buildings by Thermo-Aeraulic Zonal Approach. Build Simul 15: 99-115.
DOI: https://doi.org/10.1007/s12273-021-0781-z
Lamrani M. A., Boulard T., Roy J. C., Jaffrin A.. 2001. Airflows and Temperature Patterns Induced in a Confined Greenhouse. J Agr Eng Res 8(1):75–88.
DOI: https://doi.org/10.1006/jaer.2000.0568
Mazzeo, D., Matera N., Cornaro C., Oliveti G., Romagnoni P., De Santoli L. 2020. EnergyPlus, IDA ICE and TRNSYS Predictive Simulation Accuracy for Building Thermal Behaviour Evaluation by Using an Experimental Campaign in Solar Test Boxes with and without a PCM Module. Energ Buildings. 212:109812.
DOI: https://doi.org/10.1016/j.enbuild.2020.109812
Ogunlowo Q.O., Akpenpuun T.D., Na W.H., Rabiu A., Adesanya M.A., Addae K.S., Kim H.T., Lee H.W. 2021. Analysis of Heat and Mass Distribution in a Single- and Multi-Span Greenhouse Microclimate. J Agric. 11:891.
DOI: https://doi.org/10.3390/agriculture11090891
Ogunlowo, Qazeem Opeyemi, and Joshua Olanrewaju Olaoye. 2017. “Development and Performance Evaluation of a Guided Horizontal Conveyor Rice Harvester.” Agrosearch 17(1):66–88.
DOI: https://doi.org/10.4314/agrosh.v17i1.6
Rabiu A., Na W.H, Akpenpuun T.D., Rasheed A., Adesanya M.A, Ogunlowo Q.O., Kim H.T., Lee H.W. 2022. Determination of Overall Heat Transfer Coefficient for Greenhouse Energy-Saving Screen Using Trnsys and Hotbox. Biosys Eng. 217: 83-101.
DOI: https://doi.org/10.1016/j.biosystemseng.2022.03.002
Rafiq A., Na W.H, Rasheed A., Kim H.T., Lee H.W. 2019. Determination of Thermal Radiation Emissivity and Absorptivity of Thermal Screens for Greenhouse. Protect. Hortic. Plant Factory. 28(4):311–21.
DOI: https://doi.org/10.12791/KSBEC.2019.28.4.311
Rafiq A., Na W.H, Rasheed A., Lee J.W., Kim H.T., Lee H.W. 2021. Measurement of Longwave Radiative Properties of Energy-Saving Greenhouse Screens. J Agr Eng LII:1209(October).
DOI: https://doi.org/10.4081/jae.2021.1209
Rasheed A., Kwak C.S., Na W.H., Lee J.W., Kim H.T., Lee H.W. 2020. Development of a Building Energy Simulation Model for Control of Multi-Span Greenhouse Microclimate. Agron. 10(9):1236.
DOI: https://doi.org/10.3390/agronomy10091236
Rasheed A., Lee J.W., Lee H.W.. 2017. Development of a Model to Calculate the Overall Heat Transfer Coefficient of Greenhouse Covers. Span J Agric Res. 15(4): e0208 – e0208.
DOI: https://doi.org/10.5424/sjar/2017154-10777
Rasheed A., Lee J.W., Lee H.W. 2018. Development and Optimization of a Building Energy Simulation Model to Study the Effect of Greenhouse Design Parameters. Energies 11(8):2001.
DOI: https://doi.org/10.3390/en11082001
Rasheed A., Na W.H, Lee J.W., Kim H.T., Lee H.W. 2019. Optimization of Greenhouse Thermal Screens for Maximized Energy Conservation. Energies 12(19): 3592.
DOI: https://doi.org/10.3390/en12193592
Sanft R., Walter A. 2020. Exploring Mathematical Modeling in Biology Through Case Studies and Experimental Activities. Elsevier Inc. pp 154 - 5
Shamshiri R. 2007.Principles of Greenhouse Control Engineering: Theories and Concepts. Inst. of Adv Tech. University of Putra, Malaysia.
Sunmin K. 2021. Water Engineering Modelling and Mathematics Tools. Elsevier. Inc. pp 377
TRANSSOLAR Energietechnik. 2017. Multizone Building Modeling with Type56 and TRNBuild. Trnsys 18. 5:49–50.
Villagrán E. A., Romero E.J.B., Bojacá C.R. 2019. Transient CFD Analysis of the Natural Ventilation of Three Types of Greenhouses Used for Agricultural Production in a Tropical Mountain Climate. Biosys Eng. 188:288–304.
DOI: https://doi.org/10.1016/j.biosystemseng.2019.10.026
Ward R., Choudhary R., Cundy C., Johnson G., Mcrobie A.. 2015. Simulation of Plants in Buildings; Incorporating Plant-Air Interactions in Building Energy Simulation. Proc. 14th Intern Conf IBPSA - Building Simulation. 2256–63.
Zhao Y, Teitel, M, Barak, M. 2001. Vertical Temperature and Humidity Gradients in a Naturally Ventilated Greenhouse. J Agr Eng Res. 78(4):431–36.
DOI: https://doi.org/10.1006/jaer.2000.0649
How to Cite
Ogunlowo, Q. O. (2022) “Effect of envelope characteristics on the accuracy of discretised greenhouse model in TRNSYS”, Journal of Agricultural Engineering, 53(3). doi: 10.4081/jae.2022.1420.
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- L. Vecchione, M. Moneti, S. Cocchi, M. Villarini, M. Sisinni, A. Micangeli, Parametric experimental tests of steam gasification of pine wood in a fluidized bed reactor , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Gianluca Burchi, Sonia Cacini, Marco Fedrizzi, Mauro Pagano, Mirko Guerrieri, Temperature conditioning in ornamental plant production with a prototype device: root zone cooling in protected environments , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Francesco Barreca, Pasquale Praticò, Environmental indoor thermal control of extra virgin olive oil storage room with phase change materials , Journal of Agricultural Engineering: Vol. 50 No. 4 (2019)
- Najmun Nahar, Alex Otieno Owino, Sayful Kabir Khan, Zakaria Hossain, Noma Tamaki, Effects of controlled burn rice husk ash on geotechnical properties of the soil , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Ye-jin Seo, Eunmi Hong, Jong-hyuk Lee, Young-joon Jeong, Byung-hun Seo, Soojin Jun, Seung-Jae Lee, Won Choi, Comparative study of coating agents for prevention of fine-dust-induced light transmittance reduction in greenhouse covering materials , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Giacomo Scarascia-Mugnozza, Silvana Fuina, Sergio Castellano, Structural design and experimental tests on a model of tensegrity greenhouse prototype , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Aldo Calcante, Roberto Oberti, Francesco M. Tangorra, Definition of linear regression models to calculate the technical parameters of Italian agricultural tractors , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Giacomo Costagli, Matteo Betti, Avocado oil extraction processes: method for cold-pressed high-quality edible oil production versus traditional production , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Andrea Setti, Giulio Castelli, Lorenzo Villani, Roberto Ferrise, Elena Bresci, Modelling the impacts of water harvesting and climate change on rainfed maize yields in Senegal , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Bing Li, Jiyun Li, Key technology of crop precision sowing based on the vision principle , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
<< < 13 14 15 16 17 18 19 20 21 22 > >>
You may also start an advanced similarity search for this article.
