Artifical Neural Network for Forecasting Electricity Consumption in Energy Enterprises

Abstract

The concept of "Digital Transformation 2030", which defines the national goals and strategic objectives of the development of the Russian Federation for the period up to 2030, specifies specialized goals and objectives. These goals and objectives are an important message for the introduction of intelligent information management systems based on digital technologies in the electric power industry in general and energy consumption in particular. The main challenges for the transition to digital transformation are: increasing the rate of tariff growth for the end consumer; increasing deterioration of the network infrastructure; the presence of excessive network construction; increasing requirements for the quality of energy consumption. Based on the above, this article is devoted to the development of a method for the formation of an intelligent con­trol system at an energy enterprise by obtaining an early forecast of the amount of electricity required. The predicted values will help not only to increase the energy efficiency of the company through the implementation of specialized energy saving measures, but also to reduce the financial costs of electricity. The solution to this problem is presented in the form of an intelligent neural network system. The main advantages of this artificial neural network are versatility, high-speed and accurate learning, as well as a small amount of training data. The artificial neural network itself is based on the freely distributed TensorFlow machine learning software library, and a modified error backpropagation method is used as training, the main difference of which is the addition of an artificial neural network learning rate increase factor. The results of the analysis of the effectiveness of the method showed that the proposed intelligent neural network system is more accurate (including relative to similar solutions): the average error does not exceed 3.08 % over the entire time of the experiment. This will allow companies to carry out energy saving measures, which will be especially useful in the current economic realities.