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

Abstract

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.

Modelling the Temperature Inside a Greenhouse Tunnel

“…When focusing on the analytical model, it is clear that it is accurate when compared to other literature, in particular, Nauta et al [34] in which they achieved an RMSE of 4.25 • C. In Tong et al [36], the authors used far more sensors and more cumbersome thermodynamic and heat transfer equations to produce an error of 1.0 • C at night and 1.5 • C during the day. Therefore, it is clear that although the models are not as accurate as the models developed in the literature, they have a low computational cost and simpler implementation of modelling techniques that can lead to similar results and a higher resolution of predictions that can aid in decision making in near real time.…”

“…Similarly, Mobtaker et al [33] used multiple different orientations and shapes of greenhouses to determine optimal conditions for energy saving in a greenhouse. Unlike Nauta et al [34], they investigated the effect that variables inside of a greenhouse have on one another. Using radiation networks, they [33] describe the effects that the greenhouse cover, soil, and a brick wall they placed on the north wall of the greenhouse have on each other.…”

“…Nauta et al [34] explored the thermal modelling of an open-sided greenhouse tunnel in Guelph, Canada. They argue that CFD models are able to predict only one operational condition in a greenhouse tunnel, while being computationally intensive and are particularly difficult to compute without initial conditions.…”

“…In total, 20 different equations with multiple boundary conditions were used in the CFD modelling software to produce the model. Although accuracy is important for these models, it is noteworthy how relatively less equipment is used in lumped-capacitance models, such as in Nauta et al [34], to produce errors of about 4.25 • C.…”

Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses

“…The crops also release water vapor through transpiration, which affects the humidity and temperature inside the greenhouse. The crop canopy can also affect the distribution of solar radiation inside the greenhouse, leading to variations in the temperature and growth of different parts of the crop [7]. To maintain optimal growing conditions for plants and ensure efficient energy use, it is essential to balance the amount of solar radiation entering the greenhouse with the energy consumed by the crops and the greenhouse itself.…”