Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse

The prediction of gradients in a naturally ventilated greenhouse is difficult to achieve, due to the inherently stochastic nature of the airflow. Bayesian networks are numerical uncertainty techniques that can be used to study this problem. A set of experimental data was obtained: air temperature, a...

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Autores:
Silva, Jesús
NAVARRO, EVARISTO
Varela Izquierdo, Noel
Pineda, Omar
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
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oai:repositorio.cuc.edu.co:11323/7825
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https://hdl.handle.net/11323/7825
https://repositorio.cuc.edu.co/
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repository_id_str
dc.title.spa.fl_str_mv Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
title Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
spellingShingle Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
title_short Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
title_full Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
title_fullStr Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
title_full_unstemmed Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
title_sort Bayesian networks applied to climate conditions inside a naturally ventilated greenhouse
dc.creator.fl_str_mv Silva, Jesús
NAVARRO, EVARISTO
Varela Izquierdo, Noel
Pineda, Omar
dc.contributor.author.spa.fl_str_mv Silva, Jesús
NAVARRO, EVARISTO
Varela Izquierdo, Noel
Pineda, Omar
description The prediction of gradients in a naturally ventilated greenhouse is difficult to achieve, due to the inherently stochastic nature of the airflow. Bayesian networks are numerical uncertainty techniques that can be used to study this problem. A set of experimental data was obtained: air temperature, air humidity, wind speed, and CO2 concentration at one and three meters above the ground in the growing space. The data set was discretized and used to develop a Bayesian Network model that describes the relationships between the studied variables. The model shows the differences that allow to identify the degree of dependence of the variables, as well as to quantify their inference.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-02-04T23:21:47Z
dc.date.available.none.fl_str_mv 2021-02-04T23:21:47Z
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv 10.1088/1757-899X/872/1/012028
Corporación Universidad de la Costa
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Jung, D. H., Kim, H. S., Jhin, C., Kim, H. J., & Park, S. H. (2020). Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture, 173, 105402.
[2] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371
[3] Silveira. Soil prediction using artificial neural networks and topographic attributes. Geoderma,. 2013. 2013, IEEE, págs. 192-197.
[4] Noh, D. H., An, S. Y., & Kim, J. (2017, July). Implementation of optimal greenhouse control: Multiple influences approach. In 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 261-265).
[5] Cañadas, J., Sánchez-Molina, J. A., Rodríguez, F., & del Águila, I. M. (2017). Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes. Information Processing in Agriculture, 4(1), 50-63
[6] Borunda, M., Jaramillo, O. A., Reyes, A., &Ibargüengoytia, P. H. (2016). Bayesian networks in renewable energy systems: A bibliographical survey. Renewable and Sustainable Energy Reviews, 62, 32-45.
[7] Hemming, S., de Zwart, F., Elings, A., Righini, I., &Petropoulou, A. (2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors, 19(8), 1807
[8] Yan, J., Li, X., Shi, Y., Sun, S., & Wang, H. (2019). The effect of intention analysis-based fraud detection systems in repeated supply Chain quality inspection: A context of learning and contract. Information & Management, 103177.
[9] de Wilde, P., & Tian, W. (2009, September). Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change. In Building Simulation (Vol. 2, No. 3, pp. 157-174). Tsinghua Press
[10] Søvik, A. K., Augustin, J., Heikkinen, K., Huttunen, J. T., Necki, J. M., Karjalainen, S. M., ... &Teiter, S. (2006). Emission of the greenhouse gases nitrous oxide and methane from constructed wetlands in Europe. Journal of environmental quality, 35(6), 2360-2373.
[11] Roldán, J. J., Garcia-Aunon, P., Garzón, M., De León, J., Del Cerro, J., & Barrientos, A. (2016). Heterogeneous multi-robot system for mapping environmental variables of greenhouses. Sensors, 16(7), 1018.
[12] Rasheed, A., Lee, J. W., Kim, H. T., & Lee, H. W. (2019). Efficiency of Different Roof Vent Designs on Natural Ventilation of Single-Span Plastic Greenhouse. 시설원예· 식물공장, 28(3), 225-233.
[13] Akrami, M., Javadi, A. A., Hassanein, M. J., Farmani, R., Dibaj, M., Tabor, G. R., &Negm, A. (2020). Study of the Effects of Vent Configuration on Mono-Span Greenhouse Ventilation Using Computational Fluid Dynamics. Sustainability, 12(3), 986.
[14] Yang, R., Zhang, X., Ye, X., Wang, C., & Li, X. (2020). Ventilation modes and greenhouse structures affect 222 Rn concentration in greenhouses in China. Journal of Radioanalytical and Nuclear Chemistry, 1-9
[15] Abdel-Ghany, A. M., & Al-Helal, I. M. (2020). Toward Sustainable Agriculture: Net-Houses Instead of Greenhouses for Saving Energy and Water in Arid Regions. In Sustaining Resources for Tomorrow (pp. 83-98). Springer, Cham.
[16] Tallaksen, J., Johnston, L., Sharpe, K., Reese, M., & Buchanan, E. (2020). Reducing life cycle fossil energy and greenhouse gas emissions for Midwest swine production systems. Journal of Cleaner Production, 246, 118998
[17] Esmaeli, H., &Roshandel, R. (2020). Optimal design for solar greenhouses based on climate conditions. Renewable Energy, 145, 1255-1265.
[18] Munar, E. A. V., & Aldana, C. R. B. (2019). Study of natural ventilation in a Gothic multitunnel greenhouse designed to produce rose (Rosa spp.) in the high-Andean tropic. Ornamental Horticulture, 25(2), 133-143
[19] 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. Biosystems Engineering, 188, 288-304.
[20] Sanchez, L., Vásquez, C., & Viloria, A. (2018, June). Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In International Conference on Data Mining and Big data (pp. 759-766). Springer, Cham.
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spelling Silva, JesúsNAVARRO, EVARISTOVarela Izquierdo, NoelPineda, Omar2021-02-04T23:21:47Z2021-02-04T23:21:47Z2020https://hdl.handle.net/11323/782510.1088/1757-899X/872/1/012028Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The prediction of gradients in a naturally ventilated greenhouse is difficult to achieve, due to the inherently stochastic nature of the airflow. Bayesian networks are numerical uncertainty techniques that can be used to study this problem. A set of experimental data was obtained: air temperature, air humidity, wind speed, and CO2 concentration at one and three meters above the ground in the growing space. The data set was discretized and used to develop a Bayesian Network model that describes the relationships between the studied variables. The model shows the differences that allow to identify the degree of dependence of the variables, as well as to quantify their inference.Silva, JesúsNAVARRO, EVARISTO-will be generated-orcid-0000-0003-4549-502X-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2ICMSMThttps://iopscience.iop.org/article/10.1088/1757-899X/872/1/012028/pdfBayesian networks applied to climate conditions inside a naturally ventilated greenhouseArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Jung, D. H., Kim, H. S., Jhin, C., Kim, H. J., & Park, S. H. (2020). Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture, 173, 105402.[2] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371[3] Silveira. Soil prediction using artificial neural networks and topographic attributes. Geoderma,. 2013. 2013, IEEE, págs. 192-197.[4] Noh, D. H., An, S. Y., & Kim, J. (2017, July). Implementation of optimal greenhouse control: Multiple influences approach. In 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 261-265).[5] Cañadas, J., Sánchez-Molina, J. A., Rodríguez, F., & del Águila, I. M. (2017). Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes. Information Processing in Agriculture, 4(1), 50-63[6] Borunda, M., Jaramillo, O. A., Reyes, A., &Ibargüengoytia, P. H. (2016). Bayesian networks in renewable energy systems: A bibliographical survey. Renewable and Sustainable Energy Reviews, 62, 32-45.[7] Hemming, S., de Zwart, F., Elings, A., Righini, I., &Petropoulou, A. (2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors, 19(8), 1807[8] Yan, J., Li, X., Shi, Y., Sun, S., & Wang, H. (2019). The effect of intention analysis-based fraud detection systems in repeated supply Chain quality inspection: A context of learning and contract. Information & Management, 103177.[9] de Wilde, P., & Tian, W. (2009, September). Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change. In Building Simulation (Vol. 2, No. 3, pp. 157-174). Tsinghua Press[10] Søvik, A. K., Augustin, J., Heikkinen, K., Huttunen, J. T., Necki, J. M., Karjalainen, S. M., ... &Teiter, S. (2006). Emission of the greenhouse gases nitrous oxide and methane from constructed wetlands in Europe. Journal of environmental quality, 35(6), 2360-2373.[11] Roldán, J. J., Garcia-Aunon, P., Garzón, M., De León, J., Del Cerro, J., & Barrientos, A. (2016). Heterogeneous multi-robot system for mapping environmental variables of greenhouses. Sensors, 16(7), 1018.[12] Rasheed, A., Lee, J. W., Kim, H. T., & Lee, H. W. (2019). Efficiency of Different Roof Vent Designs on Natural Ventilation of Single-Span Plastic Greenhouse. 시설원예· 식물공장, 28(3), 225-233.[13] Akrami, M., Javadi, A. A., Hassanein, M. J., Farmani, R., Dibaj, M., Tabor, G. R., &Negm, A. (2020). Study of the Effects of Vent Configuration on Mono-Span Greenhouse Ventilation Using Computational Fluid Dynamics. Sustainability, 12(3), 986.[14] Yang, R., Zhang, X., Ye, X., Wang, C., & Li, X. (2020). Ventilation modes and greenhouse structures affect 222 Rn concentration in greenhouses in China. Journal of Radioanalytical and Nuclear Chemistry, 1-9[15] Abdel-Ghany, A. M., & Al-Helal, I. M. (2020). Toward Sustainable Agriculture: Net-Houses Instead of Greenhouses for Saving Energy and Water in Arid Regions. In Sustaining Resources for Tomorrow (pp. 83-98). Springer, Cham.[16] Tallaksen, J., Johnston, L., Sharpe, K., Reese, M., & Buchanan, E. (2020). Reducing life cycle fossil energy and greenhouse gas emissions for Midwest swine production systems. Journal of Cleaner Production, 246, 118998[17] Esmaeli, H., &Roshandel, R. (2020). Optimal design for solar greenhouses based on climate conditions. Renewable Energy, 145, 1255-1265.[18] Munar, E. A. V., & Aldana, C. R. B. (2019). Study of natural ventilation in a Gothic multitunnel greenhouse designed to produce rose (Rosa spp.) in the high-Andean tropic. Ornamental Horticulture, 25(2), 133-143[19] 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. Biosystems Engineering, 188, 288-304.[20] Sanchez, L., Vásquez, C., & Viloria, A. (2018, June). Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In International Conference on Data Mining and Big data (pp. 759-766). 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