Research Article
BibTex RIS Cite
Year 2022, Volume: 5 Issue: 1, 11 - 17, 01.01.2022
https://doi.org/10.34248/bsengineering.899720

Abstract

References

  • Ahmad AS, Hassan MY, Abdullah MP, Rahman, HA, Hussin F, Abdullah H, Saidur R. 2014. A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sust Energy Rev, 33: 102-109.
  • Apergis N, Payne JE. 2010. Renewable Energy Consumption and Economic Growth: Evidence from a Panel of OECD countries. Energy Pol, 38(1): 656-660.
  • Baris K, Kucukali S. 2012. Availibility of renewable energy sources in Turkey: Current situation, potential, government policies and the EU perspective. Energy Pol, 42: 377-391.
  • Cadenas E, Rivera W. 2010. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renew Energy, 35(12): 2732-2738.
  • Chien T, Hu JL. 2007. Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Pol, 35(7): 3606-3615.
  • Ekici BB, Aksoy UT. 2009. Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw, 40(5): 356-362.
  • Ekonomou L. 2010. Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2): 512-517.
  • European Union (EU). 2019a. Eurostat statistics explained. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics (access date: October 21, 2019).
  • European Union (EU). 2019b. Eurostat glossary. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Renewable_energy_sources (access date: October 21, 2019).
  • European Union (EU). 2020. Eurostat statistics explained. https://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics#Consumption_of_renewable_energy_almost_doubled_between_2004_and_2018 (accessed date: 20.02.2020).
  • EuroStat. 2019. Share of energy from renewable sources. URL: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_ind_ren&lang=en (access date: October 21, 2019).
  • Gavin H. 2019. The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. URL: https://people.duke.edu/~hpgavin/ce281/lm.pdf (access date: August 27, 2021).
  • Hirschl B. 2009. International renewable energy policy—between marginalization and initial approaches. Energy Pol, 37(11): 4407-4416.
  • Iniyan S, Suganthi L, Samuel AA. 2006. Energy models for commercial energy prediction and substitution of renewable energy sources. Energy Pol, 34(17): 2640-2653.
  • International Energy Agency (IEA). 2019. Renewables 2019: Analysis and forecast to 2024. URL: http://www.iea.org/renewables2019 (access date: November 18, 2020).
  • Jinke L, Hualing S, Dianming G. 2008. Causality relationship between coal consumption and GDP: Difference of major OECD and non-OECD countries, Appl Energy, 85: 421-429.
  • Kalogirou S. 2009. Artificial neural networks and genetic algorithms in energy applications in buildings. Adv Build Energy Res, 3: 83-119.
  • Kialashaki A, Reisel JR. 2014. Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. Energy, 76: 749-760.
  • Kitzing L, Mitchell C, Morthorst PE. 2012. Renewable energy policies in Europe: Converging or diverging? Energy Pol, 51: 192-201.
  • Li LL, Wen SY, Tseng ML, Wang CS. 2019. Renewable energy prediction: A novel short-term prediction model of photovoltaic output power. J Clean Prod, 228: 359-375.
  • Liddle B. 2012. Breaks and trends in OECD countries' energy–GDP ratios. Energy Pol, 45: 502-509.
  • Mabel MC, Fernandez E. 2008. Analysis of wind power generation and prediction using ANN: A case study. Renew Energy, 33(5): 986-992.
  • Martins FR, Pereira EB. 2011. Enhancing information for solar and wind energy technology deployment in Brazil. Energy Pol, 39(7): 4378-4390.
  • Nagy K, Körmendi K. 2012. Use of renewable energy sources in light of the “New Energy Strategy for Europe 2011–2020”. Appl Energy, 96: 393-399.
  • Nazlioglu S, Lebe F, Kayhan S. 2011. Nuclear energy consumption and economic growth in OECD countries: Cross-sectionally dependent heterogeneous panel causality analysis. Energy Pol, 39(10): 6615-6621.
  • Scarlat N, Dallemand JF, Monforti-Ferrario F, Banja M, Motola V. 2015. Renewable energy policy framework and bioenergy contribution in the European Union–An overview from national renewable energy action plans and progress reports. Renew Sust Energy Rev, 51: 969-985.
  • Res-Legal. 2019. Legal sources on renewable energy, URL: http://www.res-legal.eu/ (access date: November 16, 2019).
  • Reuter WH, Szolgayová J, Fuss S, Obersteiner M. 2012. Renewable energy investment: Policy and market impacts. Appl Energy, 97: 249-254.
  • Tsai SB, Xue Y, Zhang J, Chen Q, Liu Y, Zhou J, Dong W. 2017. Models for forecasting growth trends in renewable energy. Renew Sust Energy Rev, 77: 1169-1178.
  • Valentine SV. 2011. Japanese wind energy development policy: Grand plan or group think? Energy Pol, 39(11): 6842-6854.
  • Wang ZH, Zeng HL, Wei YM, Zhang YX. 2012. Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy, 97: 115-123.
  • Wang ZX, He LY, Zheng HH. 2019. Forecasting the residential solar energy consumption of the United States. Energy, 178: 610-623.
  • World Bank (WB). 2021. World development indicators URL: https://databank.worldbank.org/source/world-development-indicators (access date: August 27, 2021).
  • World Energy Forum (WEF). 2019. These 11 EU states already meet their 2020 renewable energy targets. URL: https://www.weforum.org/agenda/2019/02/these-11-eu-states-already-meet-their-2020-renewable-energy-targets/ (access date: October 21, 2019).

Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks

Year 2022, Volume: 5 Issue: 1, 11 - 17, 01.01.2022
https://doi.org/10.34248/bsengineering.899720

Abstract

The increasing demand for renewable energy sources attract attention of both researchers and governments. The countries support renewable energy and technologies developed for the efficient use of renewable energy. For this reason, the assessment and prediction of renewable energy consumption is vital for governments. Furthermore, associations put forward long-term and short-term targets for countries. Therefore, European Union (EU) members provide support schemes for promoting renewable energy consumption. In this study, renewable energy consumption in EU is predicted using artificial neural networks. The World Development indicators which are renewable electricity output, energy use generated from combustible renewables and waste, electricity production from oil, gas and coal sources, energy use generated from alternative and nuclear energy, electricity production from renewable sources excluding hydroelectric, energy imports, energy use, gross domestic product (GDP) and population are evaluated as independent variables using historical data from 1990 to 2015. The results indicate that artificial neural networks provides convenient results in energy demand forecasting as seen in similar studies of the literature.

References

  • Ahmad AS, Hassan MY, Abdullah MP, Rahman, HA, Hussin F, Abdullah H, Saidur R. 2014. A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sust Energy Rev, 33: 102-109.
  • Apergis N, Payne JE. 2010. Renewable Energy Consumption and Economic Growth: Evidence from a Panel of OECD countries. Energy Pol, 38(1): 656-660.
  • Baris K, Kucukali S. 2012. Availibility of renewable energy sources in Turkey: Current situation, potential, government policies and the EU perspective. Energy Pol, 42: 377-391.
  • Cadenas E, Rivera W. 2010. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renew Energy, 35(12): 2732-2738.
  • Chien T, Hu JL. 2007. Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Pol, 35(7): 3606-3615.
  • Ekici BB, Aksoy UT. 2009. Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw, 40(5): 356-362.
  • Ekonomou L. 2010. Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2): 512-517.
  • European Union (EU). 2019a. Eurostat statistics explained. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics (access date: October 21, 2019).
  • European Union (EU). 2019b. Eurostat glossary. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Renewable_energy_sources (access date: October 21, 2019).
  • European Union (EU). 2020. Eurostat statistics explained. https://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics#Consumption_of_renewable_energy_almost_doubled_between_2004_and_2018 (accessed date: 20.02.2020).
  • EuroStat. 2019. Share of energy from renewable sources. URL: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_ind_ren&lang=en (access date: October 21, 2019).
  • Gavin H. 2019. The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems. URL: https://people.duke.edu/~hpgavin/ce281/lm.pdf (access date: August 27, 2021).
  • Hirschl B. 2009. International renewable energy policy—between marginalization and initial approaches. Energy Pol, 37(11): 4407-4416.
  • Iniyan S, Suganthi L, Samuel AA. 2006. Energy models for commercial energy prediction and substitution of renewable energy sources. Energy Pol, 34(17): 2640-2653.
  • International Energy Agency (IEA). 2019. Renewables 2019: Analysis and forecast to 2024. URL: http://www.iea.org/renewables2019 (access date: November 18, 2020).
  • Jinke L, Hualing S, Dianming G. 2008. Causality relationship between coal consumption and GDP: Difference of major OECD and non-OECD countries, Appl Energy, 85: 421-429.
  • Kalogirou S. 2009. Artificial neural networks and genetic algorithms in energy applications in buildings. Adv Build Energy Res, 3: 83-119.
  • Kialashaki A, Reisel JR. 2014. Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States. Energy, 76: 749-760.
  • Kitzing L, Mitchell C, Morthorst PE. 2012. Renewable energy policies in Europe: Converging or diverging? Energy Pol, 51: 192-201.
  • Li LL, Wen SY, Tseng ML, Wang CS. 2019. Renewable energy prediction: A novel short-term prediction model of photovoltaic output power. J Clean Prod, 228: 359-375.
  • Liddle B. 2012. Breaks and trends in OECD countries' energy–GDP ratios. Energy Pol, 45: 502-509.
  • Mabel MC, Fernandez E. 2008. Analysis of wind power generation and prediction using ANN: A case study. Renew Energy, 33(5): 986-992.
  • Martins FR, Pereira EB. 2011. Enhancing information for solar and wind energy technology deployment in Brazil. Energy Pol, 39(7): 4378-4390.
  • Nagy K, Körmendi K. 2012. Use of renewable energy sources in light of the “New Energy Strategy for Europe 2011–2020”. Appl Energy, 96: 393-399.
  • Nazlioglu S, Lebe F, Kayhan S. 2011. Nuclear energy consumption and economic growth in OECD countries: Cross-sectionally dependent heterogeneous panel causality analysis. Energy Pol, 39(10): 6615-6621.
  • Scarlat N, Dallemand JF, Monforti-Ferrario F, Banja M, Motola V. 2015. Renewable energy policy framework and bioenergy contribution in the European Union–An overview from national renewable energy action plans and progress reports. Renew Sust Energy Rev, 51: 969-985.
  • Res-Legal. 2019. Legal sources on renewable energy, URL: http://www.res-legal.eu/ (access date: November 16, 2019).
  • Reuter WH, Szolgayová J, Fuss S, Obersteiner M. 2012. Renewable energy investment: Policy and market impacts. Appl Energy, 97: 249-254.
  • Tsai SB, Xue Y, Zhang J, Chen Q, Liu Y, Zhou J, Dong W. 2017. Models for forecasting growth trends in renewable energy. Renew Sust Energy Rev, 77: 1169-1178.
  • Valentine SV. 2011. Japanese wind energy development policy: Grand plan or group think? Energy Pol, 39(11): 6842-6854.
  • Wang ZH, Zeng HL, Wei YM, Zhang YX. 2012. Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy, 97: 115-123.
  • Wang ZX, He LY, Zheng HH. 2019. Forecasting the residential solar energy consumption of the United States. Energy, 178: 610-623.
  • World Bank (WB). 2021. World development indicators URL: https://databank.worldbank.org/source/world-development-indicators (access date: August 27, 2021).
  • World Energy Forum (WEF). 2019. These 11 EU states already meet their 2020 renewable energy targets. URL: https://www.weforum.org/agenda/2019/02/these-11-eu-states-already-meet-their-2020-renewable-energy-targets/ (access date: October 21, 2019).
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Asma Mohamed Elmı 0000-0001-9391-3420

Ayşe Ayçim Selam 0000-0002-8840-2818

Ahmet Kubilay Atalay 0000-0003-1401-9119

Publication Date January 1, 2022
Submission Date March 21, 2021
Acceptance Date October 25, 2021
Published in Issue Year 2022 Volume: 5 Issue: 1

Cite

APA Mohamed Elmı, A., Selam, A. A., & Atalay, A. K. (2022). Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks. Black Sea Journal of Engineering and Science, 5(1), 11-17. https://doi.org/10.34248/bsengineering.899720
AMA Mohamed Elmı A, Selam AA, Atalay AK. Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks. BSJ Eng. Sci. January 2022;5(1):11-17. doi:10.34248/bsengineering.899720
Chicago Mohamed Elmı, Asma, Ayşe Ayçim Selam, and Ahmet Kubilay Atalay. “Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks”. Black Sea Journal of Engineering and Science 5, no. 1 (January 2022): 11-17. https://doi.org/10.34248/bsengineering.899720.
EndNote Mohamed Elmı A, Selam AA, Atalay AK (January 1, 2022) Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks. Black Sea Journal of Engineering and Science 5 1 11–17.
IEEE A. Mohamed Elmı, A. A. Selam, and A. K. Atalay, “Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks”, BSJ Eng. Sci., vol. 5, no. 1, pp. 11–17, 2022, doi: 10.34248/bsengineering.899720.
ISNAD Mohamed Elmı, Asma et al. “Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks”. Black Sea Journal of Engineering and Science 5/1 (January 2022), 11-17. https://doi.org/10.34248/bsengineering.899720.
JAMA Mohamed Elmı A, Selam AA, Atalay AK. Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks. BSJ Eng. Sci. 2022;5:11–17.
MLA Mohamed Elmı, Asma et al. “Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks”. Black Sea Journal of Engineering and Science, vol. 5, no. 1, 2022, pp. 11-17, doi:10.34248/bsengineering.899720.
Vancouver Mohamed Elmı A, Selam AA, Atalay AK. Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks. BSJ Eng. Sci. 2022;5(1):11-7.

                                                24890