An Artificial Neural Network Approach to Predict Environmental Impact of Wind Energy Consumption - A case Study
Abstract
In this paper, we present an approach to predict carbon emissions and renewable energy consumption using artificial neural network (ANN). To determine the model relationships between the input variables and the expected carbon emissions and energy consumption, a multilayer forward ANN is used. Experimental results demonstrate that proposed ANN model provides accurate results between predicted and actual values. The results of this study may guide decision makers to select the most efficient combination of renewable energy sources including wind, hydropower and solar, and conventional energy sources including oil and natural gas, in order to meet the growing energy demand while reducing carbon emissions in developing countries.
References
S. Anees , H. Zaidi, D. Danish, F. Hou and F. M. Mirza, "The role of renewable and non-renewable energy consumption in CO2 emissions: a disaggregate analysis of Pakistan," Environmental Science and Pollution Research, vol. 25, pp. 31616–31629, 2018.
UCSUSA. (2017, September 24). Benefits of Renewable Energy Use. [Online]. Available: https://www.ucsusa.org/resources/benefits-renewable-energy-use.
F. Joao, C. Gustavo, J. Albert, T. Dietmar and M. Paulo, "An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers," Informs, vol. 113, no. 113, pp. 1-20, 2019.
M. H. Rezaei, M. Sadeghzadeh, M. A. Nazari, M. H. Ahmadi and F. R. Astaraei, "Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries," International Journal of Low-Carbon Technologies, vol.13, pp. 266–271, 2018.
MFA. (2019, October 5). Turkey’s Energy Profile and Strategy. [Online]. Available: http://www.mfa.gov.tr/turkeys-energy-strategy.en.mfa.
Invest. (2020, May 5). Energy. [Online]. Available: https://www.invest.gov.tr/en/Sectors/Pages/energy.aspx.
IEA. (2020, June 10). Energy Usage Data for Turkey. [Online]. Available: https://www.iea.org/data-and statistics?country=TURKEY&fuel=Energy%20supply&indicator=Electricity%20generation%20by%20source.
C. Erdin and G. Ozkaya, "Turkey’s 2023 Energy Strategies and Investment Opportunities for Renewable Energy Sources: Site Selection Based on ELECTRE," Sustainability, vol. 11, pp. 2136, 2019.
Energy. (2019, April 25). Electricity. [Online]. Available: https://www.enerji.gov.tr/en-US/Pages/Electricity.
H. Sogukpinar, I. Bozkurt and S. C. Cag, "Turkey’s Energy Strategy for 2023 Targets after 2000 MW Giant Renewable Energy Contract," ICPRE 2018: E3S Web of Conferences 64, 2018.
R. Ata, "The Current Situation of Wind Energy in Turkey," Journal of Energy, 2013.
E. Yasar and A. U. Özkan, "Turkey’s Forecasting of Energy Demand with Artificial Neural-Network," International Renewable Energy Conference, İstanbul, Turkey, 2017.
X. Jiang, H. Ling, J. Yan, B. Li and Z. Li, "Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization," Mathematical Problems in Engineering, vol. 2013.
M. Bilgili, "Estimation of Net Electricity Consumption of Turkey," J. of Thermal Science and Technology, vol.29, no. 2, pp. 89-98, 2009.
O. Vinnychuk, V. Grygorkiy and L. Makhanets, "Research Of Economic Growth In The Context Of Sustainable Development: Neural Network Approach," Business Systems and Economic, 2018.
A. Heydari, D. Garsia, F. Keynia, F. Bisegna and L. D. Santoli, "Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids Using GRNN-GWO Methodology," Energy Procedia, vol. 159, pp.154-159, 2019.
K. Mason, J. Duggan and E. Howley, "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, vol. 155(C), pp.705-720, 2018.
A. M. Hossein, J. Hamidreza, C. Kwok-Wing, K. Ravinder and M. A. Rosen, "Carbon dioxide emissions prediction of five Middle Eastern countries using artificial neural networks," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019.
M. Z. Khan and M. F. Khan, "Application of ANFIS, ANN and fuzzy time series models to CO2 emission from the energy sector and global temperature increase," International Journal of Climate Change Strategies and Management, vol. 11, no. 5, pp. 622-642,2019.
Ujjwalkarn. (2016, June 16). A Quick Introduction to Neural Networks. [Online]. Available: https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/.
A. Markopoulos, S. Georgiopoulos and D. Manolakos, "On the use of back propagation and radial basis function neural networks in surface roughness prediction," J Ind Eng Int, vol.11, pp. 389–400, 2016.
World-Nuclear-Power, (2019, July 22). World Nuclear Power. [Online]. Available: http://www.worldnuclear.org/uploadedFiles/org/WNA/Publications/Working_Group_Reports/comparison_of_lifecycle.pdf.
GCP. (2019, September 5). Global Carbon Budget. [Online]. Available: https://www.globalcarbonproject.org/carbonbudget/.
J. Rentschler, (2013, May 13). World Bank. [Online]. Available: https://blogs.worldbank.org/developmenttalk/oil-price-volatility-its-risk-economic-growth-and-development.
