Abstract
The research main purpose is to realize a short-term forecast temperature values inside metal soybean storage silos using the variables temperature and relative humidity, to predict and analyze the impulse response using vector autoregressions (VAR) with the Seemingly estimator. Unrelated Regression (SUR). The soybean storage silo is considered a multivariate system, as inside the metallic structure there are several temperature and relative humidity sensors, both located internally and externally. As a multivariate system, equations were adjusted using the vector autoregressive methodology, to capture external shocks and their influence on each variable and, determine how long this impact will take effect on the system. The forecast and response impulse show in advance the moment in which that the aeration process must be started. The system of equations points out that the prototype's external variables represented by temperature 7 (T7) and humidity (U7) directly influence other variables. After the occurrence of an external shock, endogenous variables take 4 periods of time to stabilize. An external action, whether naturally or through aeration, will take around eight hours to have an effective change in temperature and humidity. The forecast provides advance knowledge to carry out aeration in the silo, in order to keep the temperature and relative humidity controlled. As 6 periods of time are needed for the response to be carried out, ranging from hours to 12 hours, given that the observations were collected every 2 hours, this time was necessary to stabilize the variables. Keep these variables at target values to add commercial value to the product.
Keywords:
soybean; silos; temperature; humidity; autoregressive vectors; dynamic regressions
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Source: Prepared by the Authors
Source: Prepared by the Authors
Source: Prepared by the Authors