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Forecasting of Türkiye's net electricity consumption with metaheuristic algorithms

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  • Bakay, Melahat Sevgül
  • Başarslan, Muhammet Sinan

Abstract

This study advances the literature by integrating and benchmarking five state-of-the-art metaheuristic algorithms to forecast Türkiye's net electricity demand using linear and exponential models: artificial ecosystem-based optimization (AEO), grey wolf optimizer (GWO), particle swarm optimization (PSO), artificial bee colony (ABC), and Harris Hawks optimization (HHO). While metaheuristic optimization methods have been utilized in energy forecasting, this study distinguishes itself by employing the novel AEO algorithm, which has demonstrated superior performance to traditional methods in similar domains, thereby contributing a fresh perspective to electricity demand forecasting. All algorithms were trained using data from 1980 to 2009, incorporating population, gross domestic product (GDP), installed power, and gross generation variables, and tested with data from 2010 to 2019. Statistical metrics (R2, MAPE, MBE, rRMSE, and MAE) were used to evaluate algorithm performance. This study projects an annual growth rate in net electricity consumption ranging from 2.14 % to 2.59 %, with cumulative increases by 2050 ranging from 92.63 % to 120.75 %. These findings underscore the importance of proactive energy investment planning to mitigate potential economic challenges arising from significant increases in electricity consumption.

Suggested Citation

  • Bakay, Melahat Sevgül & Başarslan, Muhammet Sinan, 2025. "Forecasting of Türkiye's net electricity consumption with metaheuristic algorithms," Utilities Policy, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:juipol:v:95:y:2025:i:c:s095717872500044x
    DOI: 10.1016/j.jup.2025.101929
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    References listed on IDEAS

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    1. Kucukali, Serhat & Baris, Kemal, 2010. "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, Elsevier, vol. 38(5), pages 2438-2445, May.
    2. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    3. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    4. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    5. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2020. "Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization," Energy, Elsevier, vol. 195(C).
    6. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
    7. García, Sebastián & Parejo, Antonio & Personal, Enrique & Ignacio Guerrero, Juan & Biscarri, Félix & León, Carlos, 2021. "A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level," Applied Energy, Elsevier, vol. 287(C).
    8. Garrett-Peltier, Heidi, 2017. "Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model," Economic Modelling, Elsevier, vol. 61(C), pages 439-447.
    9. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
    10. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
    11. Merve Kayacı Çodur, 2023. "Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand," Energies, MDPI, vol. 17(1), pages 1-25, December.
    12. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    13. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
    14. Ribeiro, Luiz A. de S. & Saavedra, Osvaldo R. & Lima, Shigeaki. L. & de Matos, José G. & Bonan, Guilherme, 2012. "Making isolated renewable energy systems more reliable," Renewable Energy, Elsevier, vol. 45(C), pages 221-231.
    15. Mehmet Akif Destek, 2015. "Energy Consumption, Economic Growth, Financial Development and Trade Openness in Turkey: Maki Cointegration Test," Bulletin of Energy Economics (BEE), The Economics and Social Development Organization (TESDO), vol. 3(4), pages 162-168, December.
    16. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    17. Hamad M. Alhajeri & Abdulrahman Almutairi & Abdulrahman Alenezi & Faisal Alshammari, 2020. "Energy Demand in the State of Kuwait During the Covid-19 Pandemic: Technical, Economic, and Environmental Perspectives," Energies, MDPI, vol. 13(17), pages 1-16, August.
    18. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
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