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Methodology and Validation of a New Climate Prediction Model for Commercial and Small-Scale Greenhouses

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Responsible Engineering and Living (REAL 2022)

Part of the book series: Springer Proceedings in Energy ((SPE))

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Abstract

Modelling of the greenhouse microclimate represents a great opportunity for existing growers to find ways to optimize energy usage while maintaining temperature and humidity setpoints. Models can also be used by greenhouse designers, as different build options can be compared to ensure optimal design decisions. The cost of installing new technologies and equipment is often high: simulations can be used to provide a good estimate of the potential impact specific systems will have before investing in costly greenhouse infrastructure. This paper documents the development and validation of a thermal energy model designed to simulate the microclimate of a greenhouse based on site properties, exterior conditions, greenhouse operating protocols and heat and mass transfer relationships. A dynamic lumped capacitance model approach was used. Due to the large variety of greenhouse types and equipment, the main objective was to test the predictive performance of the model at various greenhouse sites, ranging from a small backyard greenhouse to large, more complex commercial operations. Measured timeseries data was obtained for each site, either from field studies conducted by the research team or from logged greenhouse controller data supplied by commercial greenhouse operators. Various greenhouse elements common in commercial operations, such as supplemental heating and lighting, forced and passive ventilation, evaporative cooling pads, and dehumidification equipment, are found in some of the test sites, and all are incorporated into the model. The studied greenhouses are all located in southern Ontario, Canada (42.0 °N, 82.8 °W to 43.2 °N, 79.4 °W). This region is characterized as a humid, continental climate with four recognizable seasons (summer, fall, winter and spring). The model was tested with data from each season, since the regional industry is moving towards year-round production. The accuracy of the model results are quantified by using the root mean squared error (RMSE) and mean absolute error (MAE) between measured and simulated values for each test case. The accuracy of greenhouse air temperature and humidity predictions compared favorably with examples in the literature for lumped parameter greenhouse models for all greenhouse sites and seasons simulated. The results support the conclusion that the model is sufficiently accurate to be used as a design tool for growers and greenhouse designers.

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Acknowledgements

The support and assistance of the commercial greenhouse operators were essential to this study and are gratefully acknowledged. Special thanks to Dr. Fadi Al-Daoud and Dr. Chevonne Dayboll for their help in acquiring the datasets used for the validation. This study was completed as part of a larger project investigating the energy use, and potential for energy savings, in commercial horticultural greenhouses funded by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) Alliance – Tier 1 program (grant UG-T1-2020-100103 “Heat Storage to Save Energy in Ontario Greenhouses”).

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Authors and Affiliations

  1. School of Engineering, University of Guelph, 50 Stone Road E, Guelph, ON, Canada

    Alex Nauta, William David Lubitz, Syeda Humaira Tasnim & Jingjing Han

Authors
  1. Alex Nauta
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  2. William David Lubitz
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  3. Syeda Humaira Tasnim
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  4. Jingjing Han
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Corresponding author

Correspondence to Alex Nauta .

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Editors and Affiliations

  1. MAME, University of Windsor, Windsor, ON, Canada

    David S.-K. Ting

  2. Mechanical Engineering, Tennessee Tech University, Nashville, TN, USA

    Ahmad Vasel-Be-Hagh

Appendix

Appendix

The parameters used to model the six greenhouses studied in this report are shown below in Table 6.9. Several sources were consulted in this process [3, 5, 16, 20, 31, 32].

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Nauta, A., Lubitz, W.D., Tasnim, S.H., Han, J. (2023). Methodology and Validation of a New Climate Prediction Model for Commercial and Small-Scale Greenhouses. In: Ting, D.SK., Vasel-Be-Hagh, A. (eds) Responsible Engineering and Living. REAL 2022. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-031-20506-4_6

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  • DOI: https://doi.org/10.1007/978-3-031-20506-4_6

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  • Print ISBN: 978-3-031-20505-7

  • Online ISBN: 978-3-031-20506-4

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