Regression equation for estimating the maximum cooling load of a greenhouse. (1st May 2022)

• Record Type: Journal Article
• Authors: Pakari, Ali; Ghani, Saud
• Is Part Of: Solar energy. Volume 237(2022)
• Page Start: 231
• Page End: 238
• Publication Date: 2022-05-01
• Subjects: Greenhouse -- Central composite design -- EnergyPlus -- Building energy simulation -- Experiments -- Statistical model; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2022.04.006
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Developed a regression equation that predicts the cooling system size of a greenhouse. Ground thermal conductivity and cover transmission affect the cooling load the most. Average difference between regression predictions and measurements is 8.1%. Abstract: Cooling is essential for greenhouse crop cultivation in hot areas. The selection of a suitable cooling system size for greenhouses is challenging since various environmental and structural factors are involved. In this study, a regression model was developed that relate input factors, including ambient air temperature (30–44 °C), ambient relative humidity (0.15–0.5), greenhouse air temperature (20–35 °C), cover transmission (0.3–0.9), cover U value (1–6 W/m 2 K), and ground soil thermal conductivity (0.1–1.5 W/m K), to a response, the maximum cooling load of a greenhouse (W/m 2 ). The model was developed using a central composite design and the maximum cooling load was calculated using EnergyPlus. The EnergyPlus results were validated against measured cooling loads of eight experimental greenhouses. The cooling loads predicted by EnergyPlus matched the calculated cooling loads from the experimental measurements within 12.4%. While the regression equation's predictions matched the experimental measurements within 13.1%. The results showed that the effect of the factors on the cooling load in order of significance from high to low were as follows, soil thermal conductivity, cover transmission, greenhouse air temperature, ambient air temperature, cover U value, and ambient air relative humidity. The developed regression equation provides a straightforward means to predict the cooling system size for greenhouses.