Dmitry A. Kornyushkin

Saint-petersburg state university of telecommunications Prof. M.A. Bonch-Bruevich

This paper considers the possibility of implementing a neural network controller in the control system of the process of irrigation irrigation of condensers of refrigeration units used in hockey stadiums. The relevance of the study is due to the need to improve energy efficiency and reliability of cooling systems that provide maintenance of optimal temperature conditions of ice in the arena. Traditional methods of regulating the irrigation process have a number of disadvantages, such as the complexity of tuning the system parameters and low adaptability to changing operating conditions. The aim of this work is to develop and implement a neural network model capable of automatically regulating the condenser irrigation process depending on current external factors including ambient temperature, humidity and refrigeration load. In order to achieve this goal, theoretical studies were conducted to analyse existing regulation methods and experimental tests of the developed neural network on a real facility were carried out. In the course of the research, a neural network architecture was developed, including several layers of perceptrons trained on data about the refrigeration plant operation over a certain period of time. Different approaches to neural network training were considered, including the use of error back propagation algorithms and gradient descent method. The experimental part of the work showed high accuracy in predicting the optimal irrigation strategy under different operating conditions. The LSTM neural network was chosen due to its ability to account for temporal dependencies and adapt to changing operating conditions. The model was trained on historical refrigeration plant operation data including ambient temperature parameters, plant loads and coolant characteristics. Gradient descent and regularisation techniques were used during training to prevent overtraining. The results of the experiments showed that the introduction of the neural network controller allowed to significantly improve the accuracy of maintaining the set parameters of the cooling system, as well as to reduce the power consumption of the refrigeration system. In addition, a decrease in the system response time to changes in external conditions was noted, which is especially important when holding sporting events at a hockey stadium.

neural network controller training, irrigation system, refrigeration systems, neural network controller architecture