Application of neural networks for predicting electric load

The paper shows that the operational management of the power consumption regime is reduced to solving the problem of operational forecasting of the enterprise's load. The paper analyzes the works devoted to the forecasting of electric loads of power systems and industrial enterprises. It is shown that in order to achieve the required forecast accuracy, it is advisable to use adaptive forecasting procedures and, in particular, to use artificial neural networks. The use of artificial neural networks for forecasting the load of industrial enterprises is due to their properties, such as the ability to learn, reliability with incomplete input information, and the rapid response of the learned network to input influences. The conditions for determining the configuration of a neural network are considered. The structure of a neural network for predicting the electrical load of an industrial enterprise is presented. The process of training an artificial neural network with fitting the model to data from a retrospective sample is presented. The considered models of daily load forecasting were investigated on retrospective data on the modes of electricity consumption of a chemical enterprise with normalization of input data. The graphs of the actual total load and the forecast obtained by the artificial neural network models are presented. The research has shown that the use of an artificial neural network allows for a qualitative forecast of the enterprise's load under normal operating conditions of the equipment.