An approach that uses the theory of artificial neural networks to predict the consumption of electric energy, while minimizing the prediction error on long range preemption is presented. The simultaneous fulfillment of the conditions of a small deviation of the predicted values from the actual values and the preservation of squared errors in the set limits at a predetermined interval is achieved through well-balanced select of architecture of the neural network. The tests were conducted using the method of real data.