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

Abstract

AbstractModelling 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.