Application of neural networks for prediction of power consumption parameters

Research was carried out within the framework of the state task of the Ministry of Science and Higher Education of the Russian Federation (topic Development of wavelet analysis models for non-stationary modes of electrical networks to improve the reliability and efficiency of power supply to consumers, topic code: FENG-2023-0005). Subject of research: predictive neural network model for a daily load schedule. Purpose of the study: prediction of power consumption based on the graph of electrical loads using a hybrid neural network. Object of research: methods for predicting the parameters of the power supply system. Main results of research: the results of forecasting the parameters of the power supply system based on daily load curves are presented. The simulation was carried out in the MATLAB software package. As a forecasting tool, a neural network was used, for the training of which load values averaged over half-hour time intervals and coefficients characterizing daily load schedules were used. The results obtained showed that the hybrid network gives a fairly accurate result, thereby confirming the adequacy of using a neural network to predict power or electricity consumption. The results of the study can be used for short-term forecasting and other parameters of the power supply system.