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

Erick Meira de Oliveira and Fernando Luiz Cyrino Oliveira

Energy, 2018, vol. 144, issue C, 776-788

Abstract: 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.

Keywords: Electricity consumption; Forecasting; Bagging (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (81)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217320820
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:144:y:2018:i:c:p:776-788

DOI: 10.1016/j.energy.2017.12.049

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().