Forecasting of electricity consumption by industrial enterprises with a continuous nature of production based on the principal component method (PCA)

Abstract

Forecasting the consumption of electrical energy is important to improve its efficiency and, as a result, to improve the competitiveness of manufactured products by reducing the share of electricity costs in the cost of production. When determining the forecast indicators of electricity consumption by industrial enterprises, it is advisable to use modern high-precision forecasting methods that ensure the minimum value of the forecast error. Each enterprise must preliminarily determine, with the greatest possible accuracy, the amount and schedule of electricity consumption and then strictly adhere to them, thereby minimizing penalties and fines. The article deals with the issues of forecasting power consumption by industrial enterprises (on the example of a metallurgical enterprise) using the enlarged block diagram of the algorithm for predicting power consumption by the method of principal components (PCA) developed by the authors. Comparisons of the actual and predicted power consumption according to the developed model are made. The adequacy of the model is confirmed by small discrepancies between the actual and forecast data. This allows it to be used in determining the predicted values of power consumption parameters at ferrous metallurgy enterprises.