Due to the lack of information about energy consumption in heating and cooling requirements ingreenhouses, the enthalpy was used as an approach of the energy content in the environment. A sensitivity analysis was done through seven Neural Network models for enthalpy prediction. It was found that the most important variables to explain the energy content in the greenhouse are temperature, transpiration, and heating and cooling systems, which correspond to inputs of model 4 with a MSE equal to 0.38 and 1.28 for one and two times ahead, respectively. Temperature and relative humidity data were collected from Santa Rosa, Sinaloa, from 1999 to 2009. The ellipses for the comfort zone were constructed in order to determine how far the external conditions are comparable with the optimal conditions for tomato production, and also to estimate the amount of energy required in each case to reach the comfort zone. Data analysis showed that most of the time those external conditions are below and right from the comfort zone, which pointed out a problem of high temperature and low relative humidity. Besides, the most critical months for tomato production are from July to September because the external conditions are far away from comfort zone, Therefore, it is recommended to dedicated those months for cleaning the greenhouse.

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