NEURAL NETWORK MODEL OF THE CENTRAL AIR CONDITIONING SYSTEM OF THE GRANARY
Current traditional approaches to the development of central air-conditioning systems in granaries do not ensure that these systems achieve the required energy efficiency. Modeling air heating processes is based on the use of empirical coefficients and approximate average heat transfer values, which significantly complicates the design process. The aim of the article is to create a model of the central air conditioning system of the granary, based on neural network technologies and allowing to take into account all control channels, as well as to reflect the dynamic properties and non-linear nature of air conditioning systems. For the most accurate simulation of the control object, several neural networks were constructed: with different architectures; with different learning algorithms; with a different number of neurons in the hidden layer and with different latency. During the calculations, two parameters were taken into account: temperature and air humidity. The use of neural network models demonstrated the possibility of calculating the output signal with accuracy not inferior to the traditional nonlinear dynamic models of a real controlled object, without employing classical identification methods. It is possible to move from traditional control with linear regulators to self-adjusting neural network controllers, which are self-adaptive to changing properties of the controlled object and operating conditions in automatic mode, which will generally improve the quality of control in comparison with traditional approaches.