Machine Learning Models for Forecasting Petroleum Consumption: A Review

Hamza Hussaini, Fatima U. Zambuk, Badamasi I. Ya'u, Sa'idu Abubakar, Fatima M. Kabir, Ahmad Y. Abubakar

Abstract


The development and use of intelligent methods as an emerging trend are attracting attention in academia and industries, including the petroleum industry. Forecasting petroleum consumption can go to a greater extent in helping the authority concerned to properly manage petroleum demand and supply, investment analysis, research related to energy as well as the analysis of revenue generation. In this paper, a review of the related literature for forecasting petroleum consumption has been conducted with the sole aim of establishing a gap for further research. A keyword based search has been performed to find relevant literature from various academic databases and the papers collected were reviewed to identify their areas of strengths and weaknesses. It has been found that conventional machine learning models are used the most in proposing models for forecasting petroleum consumption while deep machine learning algorithms are used the least. 


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