Analysis and Forecasting of the Primary Energy Consumption in Poland Using Deep Learning

  • Anna MANOWSKA Ph.D., Eng Silesian University of Technology, Faculty of Mining, Safety Engineering and Industrial Automation
Keywords: primary energy consumption, deep learning methods, long short-term memory, deep neural network

Abstract

Consumption of fossil energy resources were increased dramatically, due to the economic and population growth. In turn, the consumption of fossil resources causes depletion of resources and contributes to environmental pollution. The European Union's "climate neutrality" initiative requires effective energy management from the member states. By this is meant a resource-efficient and competitive economy in which there is no greenhouse gas emission and where economic growth is decoupled from resource consumption. The article analyzes the level of primary energy consumption in Poland. It was examined whether a 23% drop in energy consumption could be achieved in 2030 compared to the base year and according with energy efficiency assumptions. A methodology for forecasting primary energy consumption based on deep neural networks, in particular on Long Short Term Memory (LSTM) algorithms was also presented.

Published
2020-09-09
How to Cite
MANOWSKA, A. (2020). Analysis and Forecasting of the Primary Energy Consumption in Poland Using Deep Learning . Test, 1(1), 217–222. https://doi.org/10.29227/IM-2020-01-77