In this article, a single-circuit solar water heater with a thermosiphon was built, tested and numerically modeled in Kazakhstan, Almaty. To heat cold water in the south-eastern region of Kazakhstan, a flat solar collector was developed and studied, as well as a mathematical model of a single-circuit solar installation with a thermosiphon. In this mathematical model, the Bernoulli equation was used to solve the water flow in the dispenser tank and in the collector itself. Numerical modeling in MatLab was developed using a mathematical model. The dependences of the temperature inside the solar collector, which is usually distributed inside the collector in accordance with the law of thermodynamics, were obtained, and the maximum relative humidity, which was 75%, was also solved. In the course of the study, the annual change in the efficiency of the system was decided.
This article explores the use of machine learning algorithms to identify anomalies in the solar heating system. A solar heating system that has been developed consists of several parts to simplify the description and modeling process. The authors propose a new architecture for neural networks based on ordinary differential equations. The idea is to apply the new architecture for practical problems of accident prediction (the problem of extrapolation of time series) and classification (classification of accidents based on historical data). The developed machine learning algorithms, artificial intelligence techniques, the theory of differential equations - these directions allow us to build a model for predicting the system's accident rate. The theory of database management (non-relational databases) - these systems allow you to establish the optimal storage of large time series.
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