Short-term forecasting of the time series of electricity prices with ensemble algorithms

Abstract

This article presents the results of using ensemble algorithms for short-term hourly forecasting of electricity prices. Combining forecasts has proved itself to be the approach that is most useful in the following situations. There is uncertainty in choosing the most accurate forecasting method. There is uncertainty associated with the choice of input data and factors that should be taken into account when forecasting, it is necessary to avoid large forecasting errors, both in the direction of overstatement and in the direction of understatement of the studied indicator. The article describes the author’s software implementation of the ensemble model of forecasting the time series (TS) based on the adaptive method in the R environment, as well as the results of a comparative analysis of the accuracy of forecasting TS electricity prices using the single (Holt-Winters, ARIMA) and ensemble models (OPERA, adaptive model). The results obtained allow concluding that the use of ensemble models in solving applied problems of forecasting time series is promising.