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

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  • Đozić, Damir J.
  • Gvozdenac Urošević, Branka D.

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.

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  • Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
    • Handle: RePEc:eee:energy:v:174:y:2019:i:c:p:488-496
    DOI: 10.1016/j.energy.2019.02.191
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    1. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    2. Qi Dong & Kai Xing & Hongrui Zhang, 2017. "Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    3. Inmaculada Martínez-Zarzoso & Aurelia Bengochea-Morancho & Rafael Morales-Lage, 2007. "The impact of population on CO 2 emissions: evidence from European countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 38(4), pages 497-512, December.
    4. Dahmani, Kahina & Dizene, Rabah & Notton, Gilles & Paoli, Christophe & Voyant, Cyril & Nivet, Marie Laure, 2014. "Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model," Energy, Elsevier, vol. 70(C), pages 374-381.
    5. Sadri, A. & Ardehali, M.M. & Amirnekooei, K., 2014. "General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN," Energy, Elsevier, vol. 77(C), pages 831-843.
    6. Osuolale, Funmilayo N. & Zhang, Jie, 2016. "Energy efficiency optimisation for distillation column using artificial neural network models," Energy, Elsevier, vol. 106(C), pages 562-578.
    7. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    8. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    9. Kyle, Page & Kim, Son H., 2011. "Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands," Energy Policy, Elsevier, vol. 39(5), pages 3012-3024, May.
    10. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    11. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    12. Sun, Wei & Xu, Yanfeng, 2016. "Financial security evaluation of the electric power industry in China based on a back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 101(C), pages 366-379.
    13. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
    14. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
    15. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    16. Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
    Full references (including those not matched with items on IDEAS)

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