TITLE:
Predicting Electric Energy Consumption for a Jerky Enterprise
AUTHORS:
Elena Kapustina, Eugene Shutov, Anna Barskaya, Agata Kalganova
KEYWORDS:
Autoregressive Integrated Moving Average Method, Artificial Neural Networks, Classification and Regression Trees, Electricity Consumption, Ener-gy Forecasting
JOURNAL NAME:
Energy and Power Engineering,
Vol.12 No.6,
June
30,
2020
ABSTRACT: Wholesale
and retail markets for electricity and power require consumers to forecast
electricity consumption at different time intervals. The study aims to increase economic efficiency of the enterprise
through the introduction of algorithm for forecasting electric energy
consumption unchanged in technological process. Qualitative forecast allows you
to essentially reduce costs of electrical energy, because power cannot
be stockpiled. Therefore, when buying excess electrical power, costs can
increase either by selling it on the balancing energy market or by maintaining reserve capacity. If the purchased power is
insufficient, the costs increase is due to the purchase of additional capacity.
This paper illustrates three methods of forecasting electric energy
consumption: autoregressive integrated moving average method, artificial neural
networks and classification and regression trees. Actual data from consuming of
electrical energy was used to make day, week and month ahead prediction.
The prediction effect of prediction
model was proved in Statistica simulation environment. Analysis of estimation
of the economic efficiency of prediction methods demonstrated that the use of
the artificial neural networks method for short-term forecast allowed reducing the cost of electricity more
efficiently. However, for mid- range predictions, the classification and regression
tree was the most efficient method for a Jerky Enterprise. The results indicate
that calculation error reduction allows decreases expenses for the purchase of
electric energy.