Abstract
Electric grids are constantly expanding, and supervisory control and management methods must also be improved and changed in order to maintain reliable and safe power supply to consumers. This article proposes a methodology for supporting the adoption of dispatch decisions on the base electrical load forecasting. The energy consumption forecast is based on a deep neural network, and depending on the value obtained, a recommendation for optimising the operation of the energy system is proposed. Thus, dispatch service employees will be able to make decisions on managing the energy system based on the recommendations received, which will increase the speed of decision-making and improve the efficiency of the entire dispatch centre. Also, intelligent data processing and the proposed decisions allow us to consider and compare the factors that may be missed because of the human factor when the information is processed directly by the dispatcher. The use of retrospective data about the consumed power, the ambient temperature, and the type of day of the week is proposed as a knowledge base for forecasting energy consumption and training a neural network. The proposed neural network made it possible to achieve a value of the average absolute error of MAPE prediction of 1.922%. The obtained accuracy allows the use of forecasting results for dispatch control.