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The use of experimental research results to teach artificial neural networks was aimed at determining the relationship between heat consumption and measured variables. The mechanism of changes in heat consumption was determined by changes in external climate parameters, microclimate conditions in the greenhouse and parameters describing the functioning of the technical equipment of the facility. The accuracy of modeling the heat consumption in the case of changes in the properties of the building's external partitions has been determined. In a greenhouse, this is related to the functioning of an additional movable curtain - a thermal screen. MLP networks - multilayer perceptrons with hidden layers proved to be particularly useful for predicting changes in heat consumption. In the analysis attention was paid to the accuracy of modeling depending on the size of the measurement data set. The sources of information are collections with different intervals between registered measurement records, which were collected during the tests carried out during the full calendar year.

Modeling of heat consumption in a greenhouse using experimental data