Forecast of electricity consumption based on an artificial neural network

Abstract

The study of the possibility of using an artificial neural network (ANN) to predict the hourly electricity consumption by the region of the Russian Federation was carried out. The data on electricity consumption and average daily temperature were selected as the initial data. The initial data were normalized so that they did not exceed the value of one. The number of training examples was 360 and each example had 3 inputs (the training dataset contained 1080 values). At the output, ANN calculated the hourly electricity consumption. A two-layer ANN was used for modeling. ANN was trained using the back propagation method and the conjugate gradient method. ANN training was carried out with an acceptable error of 0.00001 and was within 200 iterations of the conjugate gradient method. The use of an artificial neural network made it possible to satisfactorily describe the actual hourly electricity consumption. The average error was within 4%. The largest deviations of the actual and calculated values of hourly electricity consumption were observed around the 23rd, the 24th every day and amounted to 10–12%. The error increased as the forecast range increased. Calculations were carried out for a different number of neurons in the hidden layer: 7, 10, 13. These changes practically did not affect the forecast results. To increase the accuracy of the hourly forecast, it seems that the hourly temperature change during the day should be taken into account.