Прогнозирование электропотребления промышленных предприятий с помощью модели авторегрессии проинтегрированного скользящего среднего

Abstract

The problems concerned with forecasting the amount of electricity consumed by industrial enterprises are of utmost importance. An effective way of resolving conflicts between an industrial enterprise and a power supply company and preventing the occurrence of additional costs is to perform maximally accurate prediction of electricity consumption parameters, which can be achieved by reducing them for a contractual settlement period. Electricity consumption forecasting is of importance both from the technological and economical points of view, and its completeness has a direct effect on improving the competitiveness of manufactured products. This, in turn, is determined by a significant fraction of electricity expenditures in the enterprise output net cost. In determining the forecasted indicators of electricity consumption by industrial enterprises, it is advisable to use modern high-precision forecasting methods that ensure the minimum prediction error. The article addresses matters concerned with forecasting the electricity consumption by industrial enterprises with applying the autoregressive integrated moving average (ARIMA) method and using the Python 3.9 programming language (with involvement of the following software packages: statsmodels, numpy, pandas, pmdarima, and matplotlib) taking a metallurgical enterprise in the Republic of Uzbekistan as an example. An outline flowchart of electricity consumption prediction algorithm using the autoregressive integrated moving average method has been developed. The electricity consumption value predicted using the developed model is compared with the actual electricity consumption data. The adequacy of the developed models is validated by low absolute and relative errors between the actual and predicted data. An analysis of prediction error values has shown that the low prediction error in percentage terms is due to adequately developed mathematical models of electricity consumption parameters, due to which these can be applied to forecast the electricity consumption parameters at ferrous metallurgy enterprises.