Management of a local urban heat supply system based on neural network modeling taking into account the weather forecast

Introduction. The article deals with the problem of managing the heat supply of consumers, taking into account the weather forecast using a neural network. The monitoring subsystem was used to obtain statistical data that was used to train the neural network. Optimal control of the temperature of the coolant allows you to save fuel especially effectively, in case of rapid changes in weather conditions. The expected fuel economy reaches 5-15 % depending on the air temperature in the season and the state of the heating network. Aim. The purpose of the research is to develop an intelligent module for the automated control system “Aurora Heat balance in GCH”. The intelligent module allows you to automatically adjust the water temperature in network, taking into account the weather forecast and when the mandatory temperature limits for consumers are met. Materials and methods. Artificial neural network is considered as the main tool that minimizes errors in manual control of the boiler room temperature. A neural network in the form of a multi-layer perceptron and a deep learning LSTM procedure were used. This made it possible to predict the temperature of the coolant taking into account the inertia of the network and the forecast of air temperature. To protect the model from overtraining, the Dropout method was used with a probability of 0.2. Results. The possibilities of neural networks to predict the optimal heating temperature of the boiler are investigated. This temperature is calculated taking into account restrictions for heat consumers and taking into account the forecast air temperature. The application of a neural network model in form of a multilayer perceptron is justified. A demonstration example of using an intelligent information system for heat supply management is presented. Conclusion. The proposed methods and models are tested on real data. This confirms the possibility of their use in the development of intelligent information systems for heat supply management.