Forecasting global coal consumption: An artificial neural network approach

Journal title

Gospodarka Surowcami Mineralnymi - Mineral Resources Management




No 4

Divisions of PAS

Nauki Techniczne


<jats:title>Abstract</jats:title><jats:p>In the 21st century, energy has become an integral part of our society and of global economic development. Although the world has experienced tremendous technological advancements, fossil fuels (including coal, natural gas, and oil) continue to be the world’s primary energy source. At the current production level, it has been estimated that coal reserves (economically recoverable) would last approximately 130 years (with the biggest reserves found in the USA, Russia, China, and India). The intricate relationship between economic growth, demographics and energy consumption (particularly in countries with coal intensive industries and heavy reliance on fossil fuels), along with the elevated amounts of greenhouse gases in the atmosphere, have raised serious concerns within the scientific community about the future of coal. Thus, various studies have focused on the development and application of forecasting methods to predict the economic prospects of coal, future levels of reserves, production, consumption, and its environmental impact. With this scope in mind, the goal of this article is to contribute to the scarce literature on global coal consumption forecasting with the aid of an artificial neural network method. This paper proposes a Multilayer Perceptron neural network (MLP) for the prediction of global coal consumption for the years 2020-2030. The MLP-based model is trained with historical data sets gathered from financial institutions, global energy authorities, and energy statistic agencies, covering the years 1970 through 2016. The results of this study show a deceleration in global coal consumption for the years 2020 (3 932 Mtoe), 2025 (4 069 Mtoe) and 2030 (4 182 Mtoe).</jats:p>


Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN ; Instytut Gospodarki Surowcami Mineralnymi i Energią PAN




ISSN 0860-0953