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Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035

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  • Şenol, Halil
  • Çolak, Emre
  • Oda, Volkan

Abstract

Being among the developing countries, Türkiye's electrical needs are increasing day by day with its increase in population. To address this need, consideration should be given to bovine manure-based biogas potential (BMBP), which is a renewable energy source, particularly as one of Türkiye's national livelihoods – which is dependent on foreign countries for energy – is livestock farming. Although there are significant numbers of bovine stock distributed across the various geographical regions of Türkiye, there are no studies to date that have attempted to determine the BMBPs of these regions, either for past or future years. The aim of the current study was to calculate for the first time the BMBP for seven different regions of Türkiye between 2004 and 2021 and to estimate these for 2022 to 2035 using Artificial Neural Networks (ANN), autoregressive integrated moving averages (ARIMA) from time series, and linear regression models. Türkiye's BMBP for 2021 has been calculated to be 14,262 GWh/year, which corresponds to approximately 3.2 % of the total renewable energy used by Türkiye in 2021. For 2035, Türkiye's BMBP has been forecast to be 19,905 GWh/year according to ANN's Levenberg-Marquardt algorithm, 16,862 GWh/year according to the Bayesian Regularization algorithm, 19,329 GWh/year according to ARIMA, and 19,897 GWh/year according to the Linear Regression method. All the models proposed for predicting the BMBPs for different geographical regions in Türkiye for the coming years of interest were found to perform extremely well (MSE = 205–7917 and MAPE = 0.919–4.430). When evaluated on a regional basis, the highest BMBP for 2021 was forecasted to be 3163 GWh/year for the Central Anatolia region, which corresponded to approximately 7 % of its total electricity consumption. Considering these values, it is clear that BMBP can provide significant savings with regard to the volume of electrical energy Türkiye will require in the coming years.

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  • Şenol, Halil & Çolak, Emre & Oda, Volkan, 2024. "Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035," Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:energy:v:302:y:2024:i:c:s0360544224017225
    DOI: 10.1016/j.energy.2024.131949
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    as
    1. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
    2. Lovrak, Ana & Pukšec, Tomislav & Grozdek, Marino & Duić, Neven, 2022. "An integrated Geographical Information System (GIS) approach for assessing seasonal variation and spatial distribution of biogas potential from industrial residues and by-products," Energy, Elsevier, vol. 239(PB).
    3. Bórawski, Piotr & Holden, Lisa & Bełdycka-Bórawska, Aneta, 2023. "Perspectives of photovoltaic energy market development in the european union," Energy, Elsevier, vol. 270(C).
    4. Czekała, Wojciech & Łukomska, Aleksandra & Pulka, Jakub & Bojarski, Wiktor & Pochwatka, Patrycja & Kowalczyk-Juśko, Alina & Oniszczuk, Anna & Dach, Jacek, 2023. "Waste-to-energy: Biogas potential of waste from coffee production and consumption," Energy, Elsevier, vol. 276(C).
    5. Şenol, Halil & Ali Dereli̇, Mehmet & Özbilgin, Ferdi, 2021. "Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    7. Tumen Ozdil, N.F. & Caliskan, M., 2022. "Energy potential from biomass from agricultural crops: Development prospects of the Turkish bioeconomy," Energy, Elsevier, vol. 249(C).
    8. Demirbas, Ayhan, 2008. "Importance of biomass energy sources for Turkey," Energy Policy, Elsevier, vol. 36(2), pages 834-842, February.
    9. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    10. Janaina Camile Pasqual & Harry Alberto Bollmann & Christopher A. Scott & Thiago Edwiges & Thais Carlini Baptista, 2018. "Assessment of Collective Production of Biomethane from Livestock Waste for Urban Transportation Mobility in Brazil and the United States," Energies, MDPI, vol. 11(4), pages 1-19, April.
    11. Hasan Ertop & Atilgan Atilgan & Joanna Kocięcka & Anna Krakowiak-Bal & Daniel Liberacki & Burak Saltuk & Roman Rolbiecki, 2023. "Calculation of the Potential Biogas and Electricity Values of Animal Wastes: Turkey and Poland Case," Energies, MDPI, vol. 16(22), pages 1-19, November.
    12. Ozgur, M. Arif, 2008. "Review of Turkey's renewable energy potential," Renewable Energy, Elsevier, vol. 33(11), pages 2345-2356.
    13. Chowdhury, Hemal & Chowdhury, Tamal & Miskat, Monirul Islam & Hossain, Nazia & Chowdhury, Piyal & Sait, Sadiq M., 2021. "Potential of biogas and bioelectricity production from Rohingya camp in Bangladesh: A case study," Energy, Elsevier, vol. 214(C).
    14. Sheikhi Kordkheili, Masoud & Rahimpour, Farshad, 2023. "Artificial neural network and semi-empirical modeling of industrial-scale Gasoil hydrodesulfurization reactor temperature profile," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 198-215.
    15. Toklu, E. & Güney, M.S. & IsIk, M. & ComaklI, O. & Kaygusuz, K., 2010. "Energy production, consumption, policies and recent developments in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(4), pages 1172-1186, May.
    16. Bolen, T.J. & Hasan, Mahmudul & Conway, Timothy & Stéphane Yaméogo, Djigui David & Sanchez, Pablo & Rahman, Arifur & Azam, Hossain, 2022. "Feasibility assessment of biogas production from the anaerobic co-digestion of cheese whey, grease interceptor waste and pulped food waste for WRRF," Energy, Elsevier, vol. 254(PA).
    17. Scarlat, Nicolae & Fahl, Fernando & Dallemand, Jean-François & Monforti, Fabio & Motola, Vicenzo, 2018. "A spatial analysis of biogas potential from manure in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 915-930.
    18. Lillo-Bravo, I. & Vera-Medina, J. & Fernandez-Peruchena, C. & Perez-Aparicio, E. & Lopez-Alvarez, J.A. & Delgado-Sanchez, J.M., 2023. "Random Forest model to predict solar water heating system performance," Renewable Energy, Elsevier, vol. 216(C).
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