The article is devoted to the research of the assessment of influencing factors and forecasting of power consumption in the regional power system, taking into account its operating modes. The analysis of existing methods of forecasting energy consumption is carried out. The choice of a forecasting method using an artificial neural network is justified. An algorithm for creating a neural network for short-term prediction of electrical load is considered. The relevance of the work is due to the requirements of the current legislation for forecasting electricity consumption in order to solve the problem of maintaining a balance of power between the generating side and the consumption of electric energy. At the same time, one of the main tasks related to the generation of electric energy and its consumption is the task of maintaining a balance of capacities. On the one hand, with an increase in the planned load, interruptions in the supply of electricity may occur, on the other hand, a decrease in electricity consumption will also lead to a decrease in the efficiency of power plants, and ultimately to an increase in the cost of electricity both for the wholesale electricity market and for the end user. The developed neural network model reduces the task of shortterm forecasting of power consumption to the search for a matrix of free coefficients by training on available statistical data (active and re-active power, ambient temperature, date and index of the day). The received NS model of short-term forecasting of power consumption of a section of the district 10 kV electric grid takes into account the factors: – time, - meteorological conditions, – disconnections of individual power supply lines of cottages, – operating mode of electricity consumers. Predictive estimates of the power consumption of the power system have been obtained based on the data of the electricity consumed by the outdoor temperature, the type of day, etc. The model for predicting the magnitude of the consumed active and reactive power is quite workable, but at this stage still has a fairly high level of forecasting error. To improve the accuracy of forecasting, it is necessary to increase the database that makes up the training sample, because at the moment the available data cover a time period of only 3–4 months. The results of the analysis showed that forecasting reactive power consumption causes the greatest difficulties.
