Modeling of a home heating control system using neural network technologies

The aim of the work is to develop methods to improve the characteristics of existing heating systems through the use of a new control algorithm based on the parallel principle of operation and using neural network technologies. The proposed control method provides for the analysis by a special computer program of feature matrices consisting of both control actions and resulting maps. The computer, which knows in advance that different images correspond to different modes, selects the appropriate mode based on the image received at the program input. This mode can be selected in such a way that there will be minimal energy consumption while providing the conditions that the user needs. An application has been developed that uses a neural network algorithm, with the ability to change settings. Sets of images have been prepared indicating class membership by characteristic features (datasets), on which the neural network was trained and tested. More than 600 experiments with a different number of learning epochs have been performed. The influence of the number of epochs on the average percentage of image recognition of building temperature regimes has been studied. To demonstrate the possibility of implementing the proposed algorithm, a control system architecture and a distributed application consisting of a main control program with a human-machine interface and a remote web application with a human-machine interface and a microservice with an access point were developed. A mock-up of the building was made with a connected room temperature control system. The experiments carried out have confirmed the effectiveness of the proposed control method. This technology provides for the possibility of switching from managing a single building to managing an array of buildings without changing the management settings of individual buildings. Using this method will increase the convenience of management, the comfort of people's living conditions and reduce energy consumption for heating.