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TÜRKİYE'DE ENERJİ TALEBİNİN FARKLI METASEZGİSEL YÖNTEMLER KULLANILARAK TAHMİNİ: KARŞILAŞTIRMALI BİR ÇALIŞMA

Year 2025, Volume: 28 Issue: 1, 441 - 459, 03.03.2025

Taner Sevmiş Rasim Çekik

Abstract

Enerji talebi tahmini, özellikle hızlı sanayileşme ve kentleşme yaşayan Türkiye gibi ülkelerde enerji politikalarının şekillendirilmesinde kritik bir rol oynamaktadır. Enerji talebinin doğru bir şekilde tahmin edilmesi, enerji arz güvenliğinin sağlanmasına ve yenilenebilir enerji kaynaklarına geçişte stratejik yatırımların yönlendirilmesine yardımcı olur. Bu çalışma, Türkiye'nin 2035 yılına kadar olan enerji talebini tahmin etmek amacıyla modern metasezgisel optimizasyon yöntemlerinin kullanımını araştırmakta ve bu karmaşık, çok boyutlu problemi ele almadaki etkinliklerini incelemektedir. Çalışmada, 1979-2011 yıllarını kapsayan ve GSYH, nüfus, ithalat ve ihracat gibi enerji talebinin temel belirleyicilerini içeren bir veri seti kullanılmıştır. Bu veri seti üzerinde Afrika Akbabaları Optimizasyon Algoritması (AVOA), Gri Kurt Optimizasyonu (GWO), Balina Optimizasyon Algoritması (WOA) ve Dinamik Bayes Optimizasyonu (DBO) gibi çeşitli metasezgisel algoritmalar uygulanmıştır. Karşılaştırmalı analiz sonuçları, AVOA, GWO, DBO ve benzeri yaklaşımların en düşük toplam hata oranlarıyla en doğru tahminleri sağladığını göstermektedir. Analizler, AVOA metodunun 0,2391 ile en düşük toplam hatayı ve 0,3565 bağıl hata yüzdesini elde ederek doğruluk ve hesaplama verimliliği açısından diğer yöntemlerden daha iyi performans gösterdiğini ortaya koymuştur. Çalışma, enerji talebi tahminlerinde metasezgisel yaklaşımların önemli bir rol oynadığını vurgulamakta ve Türkiye’nin enerji tüketim eğilimlerini etkileyen kritik faktörleri belirleyerek gelecekteki politika kararlarına ışık tutmaktadır. Bulgular, uzun vadeli enerji planlamasının daha etkili hale getirilmesine ve sürdürülebilir enerji politikalarının geliştirilmesine katkı sağlamayı amaçlamaktadır.

Keywords

Enerji talebi Türkiye metasezgisel optimizasyon

References

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  • Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408.
  • Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158.
  • Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.
  • Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A. A., & Gandomi, A. H. (2021). Aquila optimizer: a novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250.
  • Agarwal, T., & Kumar, V. (2022). A systematic review on bat algorithm: Theoretical foundation, variants, and applications. Archives of Computational Methods in Engineering, 1–30.
  • Agency, I. E. (2009). World energy outlook. OECD/IEA Paris.
  • Akter, A., Zafir, E. I., Dana, N. H., Joysoyal, R., Sarker, S. K., Li, L., … Kamwa, I. (2024). A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation. Energy Strategy Reviews, 51, 101298.
  • Aydilek, I. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232–249.
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  • Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1461.
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  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.
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  • Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697.
  • Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.
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  • Koç, İ., Nureddın, R., & Kahramanlı, H. (2018). Türkiye’de Enerji Talebini Tahmin Etmek İçin Doğrusal Form Kullanarak GSA (Yerçekimi Arama Algoritmasi) ve IWO (Yabani Ot Optimizasyon Algoritmasi) Tekniklerinin Uygulanmasi. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 6(4), 529–543.
  • Kunkle, B. W., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., … Amlie-Wolf, A. (2019). Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature Genetics, 51(3), 414–430.
  • Li, G., Zhang, T., Tsai, C.-Y., Yao, L., Lu, Y., & Tang, J. (2024). Review of the metaheuristic algorithms in applications: Visual analysis based on bibliometrics (1994–2023). Expert Systems with Applications, 124857.
  • Martí, R., Sevaux, M., & Sörensen, K. (2024). 50 years of metaheuristics. European Journal of Operational Research.
  • Martín-Santamaría, R., López-Ibáñez, M., Stützle, T., & Colmenar, J. M. (2024). On the automatic generation of metaheuristic algorithms for combinatorial optimization problems. European Journal of Operational Research.
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.
  • Özdemir, D., & Dörterler, S. (2022). An adaptive search equation-based artificial bee colony algorithm for transportation energy demand forecasting. Turkish Journal of Electrical Engineering and Computer Sciences, 30(4), 1251–1268.
  • Özdemir, D., Dörterler, S., & Aydın, D. (2022). A new modified artificial bee colony algorithm for energy demand forecasting problem. Neural Computing and Applications, 34(20), 17455–17471.
  • Pamuk, N. (2024). Techno-economic feasibility analysis of grid configuration sizing for hybrid renewable energy system in Turkey using different optimization techniques. Ain Shams Engineering Journal, 15(3), 102474.
  • Passino, K. M. (2012). Bacterial foraging optimization. In Innovations and Developments of Swarm Intelligence Applications (pp. 219–234). IGI Global.
  • Peña-Delgado, A. F., Peraza-Vázquez, H., Almazán-Covarrubias, J. H., Torres Cruz, N., García-Vite, P. M., Morales-Cepeda, A. B., & Ramirez-Arredondo, J. M. (2020). A Novel Bio‐Inspired Algorithm Applied to Selective Harmonic Elimination in a Three‐Phase Eleven‐Level Inverter. Mathematical Problems in Engineering, 2020(1), 8856040.
  • RC, K. J. E. (1995). Particle swarm optimization. Proc IEEE Int Conf Neural Networks, 4, 1942–1948.
  • Resende, M. G. C., & Ribeiro, C. C. (2016). Optimization by GRASP. Springer.
  • Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., & Del Ser, J. (2015). One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Conversion and Management, 99, 62–71.
  • Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search–Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445–452.
  • Sarzaeim, P., Bozorg-Haddad, O., & Chu, X. (2018). Teaching-learning-based optimization (TLBO) algorithm. Advanced Optimization by Nature-Inspired Algorithms, 51–58.
  • Shehadeh, H. A. (2023). Chernobyl disaster optimizer (CDO): A novel meta-heuristic method for global optimization. Neural Computing and Applications, 35(15), 10733–10749.
  • Sonmez, M., Akgüngör, A. P., & Bektaş, S. (2017). Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy, 122, 301–310.
  • Sözen, A., Arcaklioğlu, E., & Özkaymak, M. (2005). Turkey’s net energy consumption. Applied Energy, 81(2), 209–221.
  • Tanyildizi, E., & Demir, G. (2017). Golden sine algorithm: a novel math-inspired algorithm. Advances in Electrical & Computer Engineering, 17(2).
  • Tiris, M. (2005). Global trends for energy. Turkish Workshop on Sustainable Development: Meeting the Challenges, JuØlich.
  • Uguz, H., Hakli, H., & Baykan, Ö. K. (2015). A new algorithm based on artificial bee colony algorithm for energy demand forecasting in Turkey. 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 56–61. IEEE.
  • Wilson, A. J., Pallavi, D. R., Ramachandran, M., Chinnasamy, S., & Sowmiya, S. (2022). A review on memetic algorithms and its developments. Electrical and Automation Engineering, 1(1), 7–12.
  • Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305–7336.
  • Yang, X.-S., & He, X. (2013). Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.
  • Yang, X.-S., & Slowik, A. (2020). Firefly algorithm. In Swarm intelligence algorithms (pp. 163–174). CRC Press.
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FORECASTING ENERGY DEMAND IN TURKEY USING DIFFERENT METAHEURISTIC METHODS: A COMPARATIVE STUDY

Year 2025, Volume: 28 Issue: 1, 441 - 459, 03.03.2025

Taner Sevmiş Rasim Çekik

Abstract

Energy demand forecasting plays a crucial role in shaping energy policies, particularly for countries like Turkey that experience rapid industrialization and urbanization. Accurately predicting energy demand helps to ensure energy supply security and to guide strategic investments, especially in transitioning towards renewable energy sources. This study explores the use of modern metaheuristic optimization methods to forecast Turkey's energy demand up to the year 2035, focusing on the effectiveness of various techniques in addressing this complex, multi-dimensional problem. The dataset used spans from 1979 to 2011 and includes economic and demographic indicators such as GDP, population, imports, and exports, which are key drivers of energy demand. Several metaheuristic algorithms, including The African Vultures Optimization Algorithm (AVOA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Dynamic Bayesian Optimization (DBO), were applied to this dataset. A comparative analysis of these methods demonstrated that AVOA, GWO, DBO, and other similar approaches yielded the most accurate predictions, with minimum total error rates. The analysis revealed that the AVOA method outperformed other methods in terms of accuracy and computational efficiency by obtaining the lowest total error of 0.2391 and relative error percentage of 0.3565. The study highlights the significant role metaheuristic approaches play in improving the accuracy of energy demand forecasts and informs future policy decisions by identifying critical factors affecting Turkey’s energy consumption patterns. The findings are expected to contribute to more effective long-term energy planning and the development of sustainable energy policies.

Keywords

Energy demand Turkey metaheuristic optimization

References

  • Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems. Knowledge-Based Systems, 262, 110248.
  • Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408.
  • Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158.
  • Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.
  • Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A. A., & Gandomi, A. H. (2021). Aquila optimizer: a novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250.
  • Agarwal, T., & Kumar, V. (2022). A systematic review on bat algorithm: Theoretical foundation, variants, and applications. Archives of Computational Methods in Engineering, 1–30.
  • Agency, I. E. (2009). World energy outlook. OECD/IEA Paris.
  • Akter, A., Zafir, E. I., Dana, N. H., Joysoyal, R., Sarker, S. K., Li, L., … Kamwa, I. (2024). A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation. Energy Strategy Reviews, 51, 101298.
  • Aydilek, I. B. (2018). A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Applied Soft Computing, 66, 232–249.
  • Bai, J., Li, Y., Zheng, M., Khatir, S., Benaissa, B., Abualigah, L., & Wahab, M. A. (2023). A sinh cosh optimizer. Knowledge-Based Systems, 282, 111081.
  • Bai, J., Nguyen-Xuan, H., Atroshchenko, E., Kosec, G., Wang, L., & Wahab, M. A. (2024). Blood-sucking leech optimizer. Advances in Engineering Software, 195, 103696.
  • Beheshti, Z., & Shamsuddin, S. M. H. (2013). A review of population-based meta-heuristic algorithms. Int. j. Adv. Soft Comput. Appl, 5(1), 1–35.
  • Bertsimas, D., & Tsitsiklis, J. (1993). Simulated annealing. Statistical Science, 8(1), 10–15.
  • Biçer, A. (2017). Enerji talep tahminine yönelik program geliştirme ve bir bölge için uygulaması.
  • Bilgen, S., Kaygusuz, K., & Sari, A. (2004). Renewable energy for a clean and sustainable future. Energy Sources, 26(12), 1119–1129.
  • Bulut, Y. M., & Yıldız, Z. (2016). Comparing energy demand estimation using various statistical methods: the case of Turkey. Gazi University Journal of Science, 29(2), 237–244.
  • Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R., & Price, K. V. (1999). New ideas in optimization. McGraw-Hill Ltd., UK.
  • Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426–436.
  • Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1461.
  • Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701–1708.
  • Ediger, V. Ş., & Tatlıdil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473–487.
  • Erdogdu, E. (2007). Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy Policy, 35(2), 1129–1146.
  • Es, H., KALENDER ÖKSÜZ, F., & Hamzacebi, C. (2014). Forecasting the net energy demand of Turkey by artificial neural networks. Journal of the Faculty of Engineering and Architecture of Gazi University, 29(3).
  • Ezugwu, A. E., Shukla, A. K., Nath, R., Akinyelu, A. A., Agushaka, J. O., Chiroma, H., & Muhuri, P. K. (2021). Metaheuristics: a comprehensive overview and classification along with bibliometric analysis. Artificial Intelligence Review, 54, 4237–4316.
  • Feoktistov, V. (2006). Differential evolution. Springer.
  • Gendreau, M. (2003). An introduction to tabu search. In Handbook of metaheuristics (pp. 37–54). Springer.
  • Guo, C., Tang, H., Niu, B., & Lee, C. B. P. (2021). A survey of bacterial foraging optimization. Neurocomputing, 452, 728–746.
  • Güven, A. F., Yörükeren, N., Tag-Eldin, E., & Samy, M. M. (2023). Multi-objective optimization of an islanded green energy system utilizing sophisticated hybrid metaheuristic approach. IEEE Access.
  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.
  • Kankal, M., Akpınar, A., Kömürcü, M. İ., & Özşahin, T. Ş. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927–1939.
  • Kar, A. K. (2016). Bio inspired computing–a review of algorithms and scope of applications. Expert Systems with Applications, 59, 20–32.
  • Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697.
  • Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.
  • Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 53(1), 75–83.
  • Koç, İ., Nureddın, R., & Kahramanlı, H. (2018). Türkiye’de Enerji Talebini Tahmin Etmek İçin Doğrusal Form Kullanarak GSA (Yerçekimi Arama Algoritmasi) ve IWO (Yabani Ot Optimizasyon Algoritmasi) Tekniklerinin Uygulanmasi. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 6(4), 529–543.
  • Kunkle, B. W., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., … Amlie-Wolf, A. (2019). Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nature Genetics, 51(3), 414–430.
  • Li, G., Zhang, T., Tsai, C.-Y., Yao, L., Lu, Y., & Tang, J. (2024). Review of the metaheuristic algorithms in applications: Visual analysis based on bibliometrics (1994–2023). Expert Systems with Applications, 124857.
  • Martí, R., Sevaux, M., & Sörensen, K. (2024). 50 years of metaheuristics. European Journal of Operational Research.
  • Martín-Santamaría, R., López-Ibáñez, M., Stützle, T., & Colmenar, J. M. (2024). On the automatic generation of metaheuristic algorithms for combinatorial optimization problems. European Journal of Operational Research.
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.
  • Özdemir, D., & Dörterler, S. (2022). An adaptive search equation-based artificial bee colony algorithm for transportation energy demand forecasting. Turkish Journal of Electrical Engineering and Computer Sciences, 30(4), 1251–1268.
  • Özdemir, D., Dörterler, S., & Aydın, D. (2022). A new modified artificial bee colony algorithm for energy demand forecasting problem. Neural Computing and Applications, 34(20), 17455–17471.
  • Pamuk, N. (2024). Techno-economic feasibility analysis of grid configuration sizing for hybrid renewable energy system in Turkey using different optimization techniques. Ain Shams Engineering Journal, 15(3), 102474.
  • Passino, K. M. (2012). Bacterial foraging optimization. In Innovations and Developments of Swarm Intelligence Applications (pp. 219–234). IGI Global.
  • Peña-Delgado, A. F., Peraza-Vázquez, H., Almazán-Covarrubias, J. H., Torres Cruz, N., García-Vite, P. M., Morales-Cepeda, A. B., & Ramirez-Arredondo, J. M. (2020). A Novel Bio‐Inspired Algorithm Applied to Selective Harmonic Elimination in a Three‐Phase Eleven‐Level Inverter. Mathematical Problems in Engineering, 2020(1), 8856040.
  • RC, K. J. E. (1995). Particle swarm optimization. Proc IEEE Int Conf Neural Networks, 4, 1942–1948.
  • Resende, M. G. C., & Ribeiro, C. C. (2016). Optimization by GRASP. Springer.
  • Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., & Del Ser, J. (2015). One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Conversion and Management, 99, 62–71.
  • Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search–Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445–452.
  • Sarzaeim, P., Bozorg-Haddad, O., & Chu, X. (2018). Teaching-learning-based optimization (TLBO) algorithm. Advanced Optimization by Nature-Inspired Algorithms, 51–58.
  • Shehadeh, H. A. (2023). Chernobyl disaster optimizer (CDO): A novel meta-heuristic method for global optimization. Neural Computing and Applications, 35(15), 10733–10749.
  • Sonmez, M., Akgüngör, A. P., & Bektaş, S. (2017). Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy, 122, 301–310.
  • Sözen, A., Arcaklioğlu, E., & Özkaymak, M. (2005). Turkey’s net energy consumption. Applied Energy, 81(2), 209–221.
  • Tanyildizi, E., & Demir, G. (2017). Golden sine algorithm: a novel math-inspired algorithm. Advances in Electrical & Computer Engineering, 17(2).
  • Tiris, M. (2005). Global trends for energy. Turkish Workshop on Sustainable Development: Meeting the Challenges, JuØlich.
  • Uguz, H., Hakli, H., & Baykan, Ö. K. (2015). A new algorithm based on artificial bee colony algorithm for energy demand forecasting in Turkey. 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 56–61. IEEE.
  • Wilson, A. J., Pallavi, D. R., Ramachandran, M., Chinnasamy, S., & Sowmiya, S. (2022). A review on memetic algorithms and its developments. Electrical and Automation Engineering, 1(1), 7–12.
  • Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305–7336.
  • Yang, X.-S., & He, X. (2013). Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.
  • Yang, X.-S., & Slowik, A. (2020). Firefly algorithm. In Swarm intelligence algorithms (pp. 163–174). CRC Press.
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There are 62 citations in total.

Details

Primary Language English
Subjects Query Processing and Optimisation, Data Mining and Knowledge Discovery, Optimization in Manufacturing
Journal Section Computer Engineering
Authors

Taner Sevmiş 0000-0003-0034-4252

Rasim Çekik 0000-0002-7820-413X

Publication Date March 3, 2025
Submission Date November 7, 2024
Acceptance Date January 31, 2025
Published in Issue Year 2025Volume: 28 Issue: 1

Cite

APA Sevmiş, T., & Çekik, R. (2025). FORECASTING ENERGY DEMAND IN TURKEY USING DIFFERENT METAHEURISTIC METHODS: A COMPARATIVE STUDY. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 441-459.
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