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Application of artificial neural networks for testing long-term energy policy targets

Author:
Đozić, Damir J., Gvozdenac Urošević, Branka D.
Source:
Energy 2019 v.174 pp. 488-496
ISSN:
0360-5442
Subject:
European Union, carbon dioxide, cost effectiveness, energy, energy policy, environmental protection, greenhouse gas emissions, neural networks, prediction
Abstract:
The paper analyses a model of the EU energy system by means of artificial neural networks. This model is based on the prediction of CO2 emissions until 2050 taking into account the current Energy Policy of the EU. The results show that artificial neural networks model this system very well and that this model has the ability to predict the behaviour of CO2 emissions. This will also enable timely response and correction of energy and economic strategy by changing the value of the relevant indicators in order to achieve the ambitious planned reductions of CO2 emissions by 2050. These plans are specified in the Energy Roadmap 2050 document of the European Commission from 2012 and promote economically cost-effective scenarios that will adapt the European Union's economy to the needs of environmental protection and the reduction of energy consumption. Several structures of Artificial Neural Networks were analysed in order to select the best one for modelling large energy systems. It was determined that the model with the Cascade Forward Back Propagation structure with numerous specific indicators can model such energy systems and predict of CO2 emissions with acceptable accuracy.
Agid:
6346385