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

Nauta, Alex; Lubitz, William David; Tasnim, Syeda Humaira; Han, Juying

doi:10.1007/978-3-031-20506-4_6

Cited by 1 publication

(4 citation statements)

References 27 publications

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“…It has also been validated successfully at six other sites in all seasons. In all studies involving commercial greenhouses, the mean absolute error (MAE) between the measured and simulated air temperatures was less than 2 °C, while the MAE for the RH was less than 8% [20]. For many greenhouses studied with the model, the MAE for air temperature and the RH were less than 1 °C and 4% [20].…”

Section: Model Configurationmentioning

confidence: 93%

“…It predicts the absolute humidity of the indoor air based on the crop type and greenhouse controls (ventilation, dehumidification technology, CO 2 burners). The model has been validated in each season against the experimental data from the site, with strong correlation for both the indoor air temperature and the RH [18,20]. It has also been validated successfully at six other sites in all seasons.…”

Section: Model Configurationmentioning

confidence: 93%

“…In all studies involving commercial greenhouses, the mean absolute error (MAE) between the measured and simulated air temperatures was less than 2 °C, while the MAE for the RH was less than 8% [20]. For many greenhouses studied with the model, the MAE for air temperature and the RH were less than 1 °C and 4% [20]. One limitation with environmental data collected from commercial greenhouses is that the sensor calibration history is not usually available to the research team.…”

Section: Model Configurationmentioning

confidence: 99%

“…This study used a lumped parameter model developed and extensively validated by the authors and described in detail in previous work [18][19][20]. The model predicts the time-series temperature of various greenhouse elements, such as the indoor air and soil, based on external weather data and system controls, using a simulation time step of six seconds.…”

Section: Model Configurationmentioning

confidence: 99%

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Performance Evaluation of a Commercial Greenhouse in Canada Using Dehumidification Technologies and LED Lighting: A Modeling Study

et al. 2023

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In this study, a lumped parameter model, developed and extensively validated by the authors, is used to simulate the impact of three different dehumidification technologies (mechanical refrigeration dehumidifier, liquid desiccant dehumidifier, and a heat recovery ventilation unit), at a commercial greenhouse growing potted roses in southwestern Ontario, Canada. Typical meteorological year (TMY) data from nearby Vineland, Ontario was used to provide the external weather data used in the model. Each greenhouse bay containing a dehumidification unit was simulated for spring, fall, and winter conditions. The potential reductions in energy use (kWh), greenhouse gas emissions (kg CO2e), and operating cost were estimated for each test case. The potential energy savings from switching from high-pressure sodium (HPS) to light-emitting diode (LED) lights were also examined. The simulation results showed that switching to LED lamps could reduce the electrical energy usage by up to 60% but would increase the space heating requirements. The expected energy-savings from using dehumidification equipment and switching from HPS to LED lighting in Canadian greenhouses is underrepresented in the literature. With the industry growing in the region, this study provides insight into the expected impact that these systems will have on the energy use in commercial greenhouses.

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Section: Model Configurationmentioning

confidence: 93%

Section: Model Configurationmentioning

confidence: 93%