Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods

In the last decades, the world's energy consumption has increased rapidly due to fundamental changes in the industry and economy. In such terms, accurate demand forecasts are imperative for decision makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. This paper expands the fields of application of combined Bootstrap aggregating (Bagging) and forecasting methods to the electric energy sector, a novelty in literature, in order to obtain more accurate demand forecasts. A comparative out-of-sample analysis is conducted using monthly electric energy consumption time series from different countries. The results show that the proposed methodologies substantially improve the forecast accuracy of the demand for energy end-use services in both developed and developing countries. Findings and policy implications are further discussed.