Enerji Sektöründe Yapay Zekâ Uygulamaları

Sevde Ertürk Çetinkaya; Gamze Genç

doi:10.37609/akya.3329

Yazarlar

Sevde Ertürk Çetinkaya

https://orcid.org/0000-0002-3048-040X

Gamze Genç

https://orcid.org/0000-0002-1133-2161

DOI: https://doi.org/10.37609/akya.3329.c9023

Özet

Nüfus artışı ve teknolojinin gelişmesi gibi sebeplerden her geçen gün artan enerji ihtiyacı beraberinde fosil kaynakların tükenmesi, küresel iklim değişikliği ve enerji güvenliği gibi problemleri de beraberinde getirmektedir. Bu problemlerin üstesinden gelmek için enerjinin daha etkin ve verimli kullanılması önem arz etmektedir. Bu bağlamda, son yıllarda her sektörde olduğu gibi enerji sektöründe de yapay zekâ teknolojileri oldukça dikkat çekmektedir. Yapay zekâ teknolojilerinin kullanımı enerji verimliliğini artırma, maliyetleri düşürme, karbon ayak izini azaltma, sürdürülebilirlik ve güvenirlik sağlama gibi hedeflere ulaşmada yenilikçi çözümler sağlayarak büyük katkılar sunmaktadır. 2053 net sıfır karbon emisyonu hedefi doğrultusunda hazırlanan eylem planları kapsamında özellikle elektrik üretim santrallerinde verimliliğin artırılmasında ve akıllı ulaşım sistemlerinin ve dijitalleşmenin enerji verimliliğine yönelik olarak bütünleşik biçimde geliştirilmesinde yapay zekâ uygulamalarının kullanımına teşvikler yer almaktadır. Kısacası, yapay zekâ teknolojileri enerji sistemlerinin daha akıllı bir şekilde kontrolünü ve optimizasyonunu sağlayarak enerji sektöründe de büyük bir vaat sunmaktadır.
Bu bölümde, enerjide yapay zekânın rolünü incelemek üzere enerji üretimi, kullanımı ve tüketimi gibi birçok alanda yapay zekânın dahil edildiği literatürdeki çalışmalar derlenmiştir. Çalışmalardan elde edilen bilgilere göre yapay zekânın enerji sistemlerine nasıl entegre edildiği, dikkate alınan parametreler ve sağladığı faydalar gibi önemli bilgiler kısaca verilmiştir.

Referanslar

Aguilar-dominguez, D., Ejeh, J., Dunbar, A. D. F., & Brown, S. F. (2021). ScienceDirect Machine learning approach for electric vehicle availability forecast to provide vehicle-to-home services. Energy Reports, 7, 71–80. https://doi.org/10.1016/j.egyr.2021.02.053

Ahmad, T., Zhang, D., Huang, C., Zhang, H., & Dai, N. (2021). Artificial intelligence in sustainable energy industry : Status Quo , challenges and opportunities. Journal of Cleaner Production, 289, 125835. https://doi.org/10.1016/j.jclepro.2021.125834

Ali, D., Yohanna, M., Puwu, M. I., & Garkida, B. M. (2016). Paci fi c Science Review A : Natural Science and Engineering Long-term load forecast modelling using a fuzzy logic approach. Pacific Science Review A: Natural Science and Engineering, 18, 123–127. https://doi.org/10.1016/j.psra.2016.09.011

Aprillia, H., Yang, H.-T., & Huang, C.-M. (2020). Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm. Energies. 13, 1879. https://doi.org/10.3390/en13081879.

Aquino, R. R. B. De, Gouveia, H. T. V, Lira, M. M. S., Ferreira, A. A., Neto, O. N., & Jr, M. A. C. (2012). Wind Forecasting and Wind Power Generation : Looking for the Best Model Based on Artificial Intelligence. The 2012 International Joint Conference on Neural Networks (IJCNN), December 2008, 1–8. https://doi.org/10.1109/IJCNN.2012.6252526

Bakkar, M., Bogarra, S., Felipe, C., Aboelhassan, A., Wang, S., & Iglesias, J. (2022). Artificial Intelligence-Based Protection for Smart Grids. Energies. 15, 4933. https://doi.org/10.3390/en15134933

Bas, J., Cirillo, C., & Cherchi, E. (2021). Technological Forecasting & Social Change Classification of potential electric vehicle purchasers : A machine learning approach. 168.

Basso, R., Kulcsár, B., & Sanchez-diaz, I. (2021). Electric vehicle routing problem with machine learning for energy prediction. Transportation Research Part B, 145, 24–55. https://doi.org/10.1016/j.trb.2020.12.007

Bedi, G., Venayagamoorthy, G. K., & Singh, R. (2016). Navigating the Challenges of Internet of Things ( IoT ) for Power and Energy Systems. 2016 Clemson University Power Systems Conference (PSC), 1–5. https://doi.org/10.1109/PSC.2016.7462853

Benhmed, K., Touati, F., Al-hitmi, M., Chowdhury, N. A., & Gonzales, A. J. S. P. (2018). PV Power Prediction in Qatar Based on Machine Learning Approach. 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), 1–4. https://doi.org/10.1109/IRSEC.2018.8702880

Bolandnazar, E., Rohani, A., & Taki, M. (2020). Environmental Effects Energy consumption forecasting in agriculture by artificial intelligence and mathematical models. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(13), 1618–1632. https://doi.org/10.1080/15567036.2019.1604872

Bose, B. K. (2017). Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems — Some Example Applications. Proceedings of the IEEE, 105(11), 2262–2273. doi: 10.1109/JPROC.2017.2756596.

Buster, G., Siratovich, P., Taverna, N., Rossol, M., Weers, J., Blair, A., Huggins, J., Siega, C., Mannington, W., Urgel, A., Cen, J., Quinao, J., Watt, R., & Akerley, J. (2021). A new modeling framework for geothermal operational optimization with machine learning (Gooml). Energies, 14(20). https://doi.org/10.3390/en14206852

Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy Sustainability in Smart Cities : Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption. 11, 1–20. https://doi.org/10.3390/en11112869

Chung, Y., Khaki, B., Li, T., Chu, C., & Gadh, R. (2019). Ensemble machine learning-based algorithm for electric vehicle user. Applied Energy, 254(August), 113732. https://doi.org/10.1016/j.apenergy.2019.113732

Daut, M. A. M., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullaha, M. P., & Hussina, F. (2017). Building electrical energy consumption forecasting analysis using conventional and arti fi cial intelligence methods : A review. Renewable and Sustainable Energy Reviews, 70(June 2015), 1108–1118. https://doi.org/10.1016/j.rser.2016.12.015

Dong, W., & Yang, Q. (2018). Multi-Step Ahead Wind Power Generation Prediction. https://doi.org/10.3390/en11081975

Dudnik, O., Vasiljeva, M., Kuznetsov, N., Podzorova, M., Nikolaeva, I., Vatutina, L., Khomenko, E., & Ivleva, M. (2021). Trends , Impacts , and Prospects for Implementing Artificial Intelligence Technologies in the Energy Industry : The Implication of Open Innovation.

Entezari, A., Aslani, A., Zahedi, R., & Noorollahi, Y. (2023). Artificial intelligence and machine learning in energy systems : A bibliographic perspective. Energy Strategy Reviews, 45(April 2022), 101017. https://doi.org/10.1016/j.esr.2022.101017

Ertürk, S., Kara, H., Akkuş, C., & Genç, G. (2023). Türkiye ’ de Farklı İklim Kuşakları İçin Yapay Sinir Ağları Kullanılarak Güneş Işınımının Tahmini. Gazi Üniversitesi Fen Bilimleri Dergisi PART C: TASARIM VE TEKNOLOJİ, 11, 885–892. https://doi.org/10.29109/gujsc.1331788

Fukushima, A., Yano, T., Imahara, S., Aisu, H., & Shimokawa, Y. (2018). Prediction of energy consumption for new electric vehicle models by machine learning. https://doi.org/10.1049/iet-its.2018.5169

Gangwani, P., Soni, J., Upadhyay, H., & Joshi, S. (2020). A Deep Learning Approach for Modeling of Geothermal Energy Prediction. International Journal of Computer Science and Information Security, 18(1), 62–65.

Georgilakis, P. S. (2007). ARTIFICIAL INTELLIGENCE SOLUTION TO ELECTRICITY PRICE FORECASTING PROBLEM. 9514. https://doi.org/10.1080/08839510701526533

Heidarpanah, M., Hooshyaripor, F., & Fazeli, M. (2023). Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market. Energy, 263(PE), 126011. https://doi.org/10.1016/j.energy.2022.126011

Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., & Amira, A. (2021). Artificial intelligence based anomaly detection of energy consumption in buildings : A review , current trends and new perspectives. Applied Energy, 287(January), 116601. https://doi.org/10.1016/j.apenergy.2021.116601

Hua, W., Chen, Y., Qadrdan, M., Jiang, J., Sun, H., & Wu, J. (2022). Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids : A review. 161.

Izgi, E., Oztopal, A., Yerli, B., Kaymak, M. K., & Sahin, A. D. (2012). Short – mid-term solar power prediction by using artificial neural networks. Solar Energy, 86, 6, 725–733. https://doi.org/10.1016/j.solener.2011.11.013

Jiang, P., Nie, Y., Wang, J., & Huang, X. (2023). Multivariable short-term electricity price forecasting using artificial intelligence and multi-input multi-output scheme. Energy Economics, 117(January 2022), 106471. https://doi.org/10.1016/j.eneco.2022.106471

Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy : Present research and future scope of Arti fi cial Intelligence. 77(April), 297–317. https://doi.org/10.1016/j.rser.2017.04.018

Kauwe, S. K., Rhone, T. D., & Sparks, T. D. (2019). Data-Driven Studies of Li-Ion-Battery Materials. Crystals, 1–9. https://doi.org/10.3390/cryst9010054

Khandakar, A., Chowdhury, M. E. H., Kazi, M.-K., Benhmed, K., Touati, F., Al-hitmi, M., & Gonzales, A. J. S. P. (2019). Machine Learning Based Photovoltaics ( PV ) Power Prediction Using Di ff erent Environmental Parameters of Qatar.

Kim, S., Jung, J., & Sim, M. K. (2019). A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning. https://doi.org/10.3390/su11051501

Lee, M. (2020). An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data. World Patent Information, 63(August), 102002. https://doi.org/10.1016/j.wpi.2020.102002

Legala, A., Zhao, J., & Li, X. (2022). Machine learning modeling for proton exchange membrane fuel cell performance. Energy and AI, 10(July), 100183. https://doi.org/10.1016/j.egyai.2022.100183

Liu, M., Liu, H., & Lee, C. (2024). An empirical study on the response of the energy market to the shock from the artificial intelligence industry. Energy, 288(October 2023), 129655. https://doi.org/10.1016/j.energy.2023.129655

Lydia, M., & Kumar, G. E. P. (2021). Materials Today : Proceedings Machine learning applications in wind turbine generating systems. Materials Today: Proceedings, 45, 6411–6414. https://doi.org/10.1016/j.matpr.2020.11.268

Mahmud, K., Azam, S., Karim, A., Zobaed, S. M., Shanmugam, B., & Mathur, D. (2021). Machine Learning Based PV Power Generation Forecasting in Alice Springs. 9. https://doi.org/10.1109/ACCESS.2021.3066494

Mancini, M., Mariani, C., & Mattia, C. (2023). Progress in Nuclear Energy Nuclear decommissioning risk management adopting a comprehensive artificial intelligence framework : An applied case in an Italian site. Progress in Nuclear Energy, 158(March 2022), 104589. https://doi.org/10.1016/j.pnucene.2023.104589

Mangla, S. K., Srivastava, P. R., Eachempati, P., & Tiwari, A. K. (2024). Exploring the impact of key performance factors on energy markets : From energy risk management perspectives. Energy Economics, 131(February), 107373. https://doi.org/10.1016/j.eneco.2024.107373

Metaxiotis, K., Kagiannas, A., Askounis, D., & Psarras, J. (2003). Artificial intelligence in short term electric load forecasting : a state-of-the-art survey for the researcher. Energy Conversion and Management, 44, 1525–1534. https://doi.org/10.1016/S0196-8904(02)00148-6.

Mhlanga, D. (2023). Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets : A Review. Energies, 16, 745. https://doi.org/10.3390/en16020745

Ming, W., Sun, P., Zhang, Z., Qiu, W., Du, J., Li, X., Zhang, Y., Zhang, G., Liu, K., Wang, Y., & Guo, X. (2023). A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring. International Journal of Hydrogen Energy, 48(13), 5197–5228. https://doi.org/10.1016/j.ijhydene.2022.10.261

Nalcaci, G., Özmen, A., & Weber, G. W. (2019). Long-term load forecasting : models based on MARS , ANN and LR methods. Central European Journal of Operations Research, 27(4), 1033–1049. https://doi.org/10.1007/s10100-018-0531-1

Nasri, A., & Gasbaoui, B. (2012). Artificial Intelligence Application ’ s for 4WD Electric Vehicle Control System. 2012(August), 243–250.

Nayak, A., & Heistrene, L. (2020). Hybrid Machine Learning Model for Forecasting Solar Power Generation. 2020 International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, 2020, pp. 910-915, doi: 10.1109/SGES51519.2020.00167.

Okoroafor, E. R., Smith, C. M., Ochie, K. I., Nwosu, C. J., Gudmundsdottir, H., & (Jabs) Aljubran, M. (2022). Machine learning in subsurface geothermal energy: Two decades in review. Geothermics, 102(March), 102401. https://doi.org/10.1016/j.geothermics.2022.102401

Omitaomu, O. A., & Niu, H. (2021). Artificial Intelligence Techniques in Smart Grid : A Survey. Smart cities, 4, 548–568. https://doi.org/10.3390/smartcities4020029

Optis, M., & Perr-sauer, J. (2019). The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production. Renewable and Sustainable Energy Reviews, 112(May), 27–41. https://doi.org/10.1016/j.rser.2019.05.031

Panapakidis, I. P., & Dagoumas, A. S. (2016). Day-ahead electricity price forecasting via the application of artificial neural network based models. Applied Energy, 172, 132–151. https://doi.org/10.1016/j.apenergy.2016.03.089

Paulescu, M., Brabec, M., Boata, R., & Badescu, V. (2017). Structured , physically inspired ( gray box ) models versus black box modeling for forecasting the output power of photovoltaic plants. Energy, 121, 792–802. https://doi.org/10.1016/j.energy.2017.01.015

Raza, M. Q., & Khosravi, A. (2015). A review on arti fi cial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352–1372. https://doi.org/10.1016/j.rser.2015.04.065

Răzuşi, P. C., & Eremia, M. (2011). Prediction of Wind Power by Artificial Intelligence Techniques. Prediction of Wind Power by Artificial Intelligence Techniques, 1, 1–6. https://doi.org/10.1109/ISAP.2011.6082239

Rigas, E. S., Member, S., Ramchurn, S. D., & Bassiliades, N. (2015). Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence : A Survey. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1619–1635. https://doi.org/10.1109/TITS.2014.2376873

Saco, A., Sundari, P. S., Karthikeyan, J., & Paul, A. (2022). An Optimized Data Analysis on a Real-Time Application of PEM Fuel Cell Design by Using Machine Learning Algorithms. Algorithms, 15(10), 1–19. https://doi.org/10.3390/a15100346

Santo, K. G. Di, Santo, S. G. Di, Monaro, R. M., & Saidel, M. A. (2018). Active demand side management for households in smart grids using optimization and arti fi cial intelligence. Measurement, 115(April 2017), 152–161. https://doi.org/10.1016/j.measurement.2017.10.010

Seyedzadeh, S., Rahimian, F. P., Glesk, I., & Roper, M. (2018). Machine learning for estimation of building energy consumption and performance : a review. Visualization in Engineering. 6, 5. https://doi.org/10.1186/s40327-018-0064-7

Shabbir, N., Ahmadiahangar, R., Kütt, L., Iqbal, M. N., & Rosin, A. (2020). Forecasting Short Term Wind Energy Generation using Machine Learning. 16–19.

Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., & Zhang, R. (2020). Artificial intelligence techniques for stability analysis and control in smart grids : Methodologies , applications , challenges and future directions. Applied Energy, 278(May), 115733. https://doi.org/10.1016/j.apenergy.2020.115733

Smart Grids, (2024) https://en.wikipedia.org/wiki/Smart_grid

Sodhro, A. H., Pirbhulal, S., Albuquerque, V. H. C. De, & Member, S. (2019). Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications. IEEE Transactions on Industrial Informatics, 15(7), 4235–4243. https://doi.org/10.1109/TII.2019.2902878

Su, D., Zheng, J., Ma, J., Dong, Z., Chen, Z., & Qin, Y. (2023). Application of Machine Learning in Fuel Cell Research. Energies, 16(11). https://doi.org/10.3390/en16114390

Sun, H., Qiu, C., Lu, L., Gao, X., Chen, J., & Yang, H. (2020). Wind turbine power modelling and optimization using artificial neural network with wind field experimental data. Applied Energy, 280(September), 115880. https://doi.org/10.1016/j.apenergy.2020.115880

Tümse, S., İlhan, A., Bilgili, M., & Şahin, B. (2022). Environmental Effects Estimation of wind turbine output power using soft computing models. https://doi.org/10.1080/15567036.2022.2066226

Wang, J., Liu, F., Song, Y., & Zhao, J. (2016). A novel model : Dynamic choice artificial neural network ( DCANN ) for an electricity price forecasting system. Applied Soft Computing Journal, 48, 281–297. https://doi.org/10.1016/j.asoc.2016.07.011

Wang, K., Qi, X., & Liu, H. (2019). Photovoltaic power forecasting based LSTM-Convolutional Network. Energy, 189, 116225. https://doi.org/10.1016/j.energy.2019.116225

Wang, Q., Xu, J., Zhang, W., Mao, M., & Wei, Z. (2018). Research progress on vanadium-based cathode materials for sodium ion batteries. 23, 8815–8838. https://doi.org/10.1039/c8ta01627e

Xu, Y., Ahokangas, P., & Louis, J. (2019). Electricity Market Empowered by Artificial Intelligence : A Platform Approach.

Zakir Hossain, S. M., Sultana, N., Haji, S., Talal Mufeez, S., Esam Janahi, S., & Adel Ahmed, N. (2023). Modeling of microbial fuel cell power generation using machine learning-based super learner algorithms. Fuel, 349(March), 128646. https://doi.org/10.1016/j.fuel.2023.128646

Zazoum, B. (2022). ScienceDirect Solar photovoltaic power prediction using different machine learning methods. Energy Reports, 8, 19–25. https://doi.org/10.1016/j.egyr.2021.11.183

Zhang, R., Feng, M., Zhang, W., Lu, S., & Wang, F. (2018). Forecast of Solar Energy Production – A Deep Learning Approach. 2018 IEEE International Conference on Big Knowledge (ICBK), 73–82. https://doi.org/10.1109/ICBK.2018.00018

Zhao, L., Shahzad, M., Hafiz, N., Nazir, M. J., & Abdalla, A. N. (2022). A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management. Environmental Science and Pollution Research, 43690–43709. https://doi.org/10.1007/s11356-022-19902-8

Zubova, N. V, & Rudykh, V. D. (2019). Optimization of power output for a wind turbine using methods of artificial intelligence Optimization of power output for a wind turbine using meth- ods of artificial intelligence. https://doi.org/10.1088/1757-899X/552/1/012034

Zulkarnain, Surjandari, I., Bramasta, R. R., & Laoh, E. (2019). Fault detection system using machine learning on geothermal power plant. 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019, 1–5. https://doi.org/10.1109/ICSSSM.2019.8887710