IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v95y2025ics095717872500044x.html

Forecasting of Türkiye's net electricity consumption with metaheuristic algorithms

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095717872500044X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2025.101929?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. Merve Kayacı Çodur, 2023. "Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand," Energies, MDPI, vol. 17(1), pages 1-25, December.
    9. 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).
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
    16. 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).
    17. 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).
    18. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Georgeta Soava & Anca Mehedintu & Mihaela Sterpu & Eugenia Grecu, 2021. "The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania," Energies, MDPI, vol. 14(9), pages 1-25, April.
    2. Ai, Hongshan & Zhong, Tenglong & Zhou, Zhengqing, 2022. "The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China," Energy Economics, Elsevier, vol. 105(C).
    3. Ahmat Khazali Acyl & Flavian Emmanuel Sapnken & Aubin Kinfack Jeutsa & Jean Marie Stevy Sama & Marcel Rodrigue Ewodo-Amougou & Jean Gaston Tamba, 2024. "Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 603-611, January.
    4. Fatin Samara & Bassam A. Abu-Nabah & Waleed El-Damaty & Mayyada Al Bardan, 2022. "Assessment of the Impact of the Human Coronavirus (COVID-19) Lockdown on the Energy Sector: A Case Study of Sharjah, UAE," Energies, MDPI, vol. 15(4), pages 1-19, February.
    5. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    6. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    7. Lazo, Joaquín & Aguirre, Gerson & Watts, David, 2022. "An impact study of COVID-19 on the electricity sector: A comprehensive literature review and Ibero-American survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Jiang, Shang & Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi, 2025. "A diffusion-model-based approach for forecasting energy demand in New Zealand’s transport sector," Applied Energy, Elsevier, vol. 400(C).
    9. 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).
    10. Hoxha, Julian & Çodur, Muhammed Yasin & Mustafaraj, Enea & Kanj, Hassan & El Masri, Ali, 2023. "Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis," Applied Energy, Elsevier, vol. 350(C).
    11. Qiao, Qingyao & Eskandari, Hamidreza & Saadatmand, Hassan & Sahraei, Mohammad Ali, 2024. "An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector," Energy, Elsevier, vol. 286(C).
    12. Neilson Luniere Vilaça & Marly Guimarães Fernandes Costa & Cicero Ferreira Fernandes Costa Filho, 2023. "A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm," Energies, MDPI, vol. 16(8), pages 1-14, April.
    13. Quintero Gutiérrez, Laura Victoria & García Rendón, John & Gutiérrez Gómez, Alejandro, 2024. "Impact of COVID-19 preventive measures on electricity demand: Evidence from Colombia," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    14. Cerqueira, Pedro André & Pereira da Silva, Patrícia, 2023. "Assessment of the impact of COVID-19 lockdown measures on electricity consumption – Evidence from Portugal and Spain," Energy, Elsevier, vol. 282(C).
    15. Micheli, Leonardo & Solas, Álvaro F. & Soria-Moya, Alberto & Almonacid, Florencia & Fernandez, Eduardo F., 2021. "Short-Term Impact of the COVID-19 Lockdown on the Energy and Economic Performance of Photovoltaics in the Spanish Electricity Sector," MPRA Paper 107969, University Library of Munich, Germany.
    16. Ağbulut, Ümit & Gürel, Ali Etem & Sarıdemir, Suat, 2021. "Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning algorithms," Energy, Elsevier, vol. 215(PA).
    17. Aguirre, Gerson & Lazo, Joaquín & Watts, David, 2023. "Regulatory response to COVID-19 in the Ibero-American electricity sector: Policy implications and critical analysis of support measures," Energy Policy, Elsevier, vol. 183(C).
    18. Garcia-Rendon, John & Rey Londoño, Felipe & Arango Restrepo, Luis José & Bohorquez Correa, Santiago, 2023. "Sectoral analysis of electricity consumption during the COVID-19 pandemic: Evidence for unregulated and regulated markets in Colombia," Energy, Elsevier, vol. 268(C).
    19. 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).
    20. Aviad Navon & Ram Machlev & David Carmon & Abiodun Emmanuel Onile & Juri Belikov & Yoash Levron, 2021. "Effects of the COVID-19 Pandemic on Energy Systems and Electric Power Grids—A Review of the Challenges Ahead," Energies, MDPI, vol. 14(4), pages 1-14, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:juipol:v:95:y:2025:i:c:s095717872500044x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.