Simulation of an Earth-Air Heat Exchanger in a Commercial Greenhouse to Improve Energy Efficiency

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

doi:10.1007/s42853-023-00188-8

Cited by 2 publications

(1 citation statement)

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“…Solar radiation is the primary source of energy for greenhouses. The amount of solar radiation that enters the greenhouse depends on various factors, such as the orientation and design of the greenhouse, the latitude of the location, and weather conditions [5]. The solar radiation that enters the greenhouse is absorbed by the crops, soil, and other surfaces inside the greenhouse, leading to an increase in the internal temperature.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (Tout), relative humidity (Hout), wind speed (W), and solar radiation (S), were used as inputs for these models, and the output was the inside temperature. The results showed that the RBF model with the LM (Levenberg–Marquardt) learning algorithm outperformed the other models, achieving the lowest error and the highest coefficient of determination (R2) value. The RBF model produced RMSE, MAPE, and R2 values of 1.32 °C, 3.23%, and 0.931, respectively. These results demonstrate that the RBF model with the LM learning algorithm can reliably predict greenhouse air temperatures for the next two hours. The ANN model can be applied to optimize time management and reduce energy losses, improving the overall efficiency of greenhouse operations.

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Section: Introductionmentioning

confidence: 99%