Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review

Abstract

This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are studied. This research also makes an analysis of which data variables are employed for training the forecasting models. Three scientific databases were consulted to perform the present research: The Directory of Open Access Journals, the Multidisciplinary Digital Publishing Institute and the Springer Link. After running the same query in the three databases and considering the period from January 2017 to December 2021, a total of 1683 articles were included in the analysis. Of these, only 13 were considered relevant for the topic under study. The results obtained showed that when compared with other areas, few papers focus on the forecasting of the prices of raw materials and stocks of companies in the field under study. Furthermore, most make use of either machine learning methodologies or time series analysis. Finally, it is also remarkable that some not only make use of existing algorithms but also develop and test new methodologies.