Araştırma Makalesi
BibTex RIS Kaynak Göster

Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı

Yıl 2026, Cilt: 16 Sayı: 1, 83 - 92, 31.01.2026

Öz

Günümüzde enerji bağımsızlığı ve sürdürülebilirlik hedefleri doğrultusunda nanoşebeke sistemleri giderek daha fazla önem kazanmaktadır. Bu sistemler, yerel enerji üretimi, depolaması ve tüketimini optimize ederek enerji verimliliğini artırmayı ve maliyetleri düşürmeyi amaçlamaktadır. Bu makalede, nanoşebekelerde yapay zeka tabanlı bir enerji yönetim sisteminin tasarımı ve MATLAB/Simulink ortamında simülasyonu sunulmaktadır. Çalışma, nanoşebeke kavramını, mikroşebekelerden farklarını ve enerji yönetim sistemlerinin kritik rolünü ele almaktadır. Yapay zeka tekniklerinin enerji yönetimindeki potansiyeli vurgulanarak, özellikle yük tahmini ve batarya yönetimi gibi alanlarda yapay sinir ağları (YSA) gibi algoritmaların kullanımı incelenmiştir. Geliştirilen model, enerji üretimi, tüketimi ve depolama verileri kullanılarak simüle edilmiş, elde edilen sonuçlar analiz edilmiş ve yapay zeka tabanlı enerji yönetim sistemlerinin klasik yöntemlere kıyasla sunduğu avantajlar değerlendirilmiştir. Simülasyon bulguları, önerilen yapay zeka tabanlı yaklaşımın nanoşebeke sistemlerinin performansını, güvenilirliğini ve ekonomik verimliliğini önemli ölçüde artırabileceğini göstermektedir.

Kaynakça

  • [1] M. Pipattanasomporn, M. Kuzlu, S. Rahman, "An Algorithm for Intelligent Home Energy Management and Demand Response Analysis," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2166-2173, 2012.
  • [2] S.A. Pourmousavi, M. H. Nehrir, "Real-Time Central Demand Response for Primary Frequency Regulation in Microgrids," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1988-1996, 2012.
  • [3] S. Parhizi, H. Lotfi, A. Khodaei, S. Bahramirad, "State of the Art in Research on Microgrids: A Review," IEEE Access, vol. 3, pp. 890-925, 2015.
  • [4] S.A.Arefifar, Y. A.-R. I. Mohamed, T. H. M. El-Fouly, "Comprehensive Operational Planning Framework for Self-Healing Control Actions in Smart Distribution Grids," IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4192-4200, 2013.
  • [5] M. E. Baran, I. El-Markaby, "A Multiagent-Based Dispatching Scheme for Distributed Generators for Voltage Support on Distribution Feeders," IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 52-59, 2007.
  • [6] S. Bahramirad, W. Reder, A. Khodaei, "Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2056-2062, 2012.
  • [7] M. Kuzlu, M. Pipattanasomporn, S. Rahman, "Review of communication technologies for smart homes/building applications," 2015 IEEE Innovative Smart Grid Technologies - Asia, pp. 1-6, 2015.
  • [8] M. R. Khan, Z. M. Haider, F. H. Malik, F. M. Almasoudi, K. S. S. Alatawi, M. S. Bhutta, “A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques,” Processes, vol. 12, no. 2, p. 270, 2024.
  • [9] M. A. Hannan, M. M. Hoque, A. Mohamed, A. Ayob, " Review of energy storage systems for electric vehicle applications: Issues and challenges," Renewable and Sustainable Energy Reviews, vol. 69, pp. 771-789, 2017.
  • [10] S. E. Eyimaya and N. Altin, “Review of energy management systems in microgrids,” Applied Sciences, vol. 14, no. 3, p. 1249, 2024.
  • [11] N. Einabadi and M. Kazerani, “Nanogrids in modern power systems: A comprehensive review,” Smart Cities, vol. 8, no. 1, p. 11, 2025.
  • [12] M.C.Di Piazza, “Volume II: Energy management systems for optimal operation of electrical micro/nanogrids,” Energies, vol. 17,p.1811, 2024.
  • [13] H. Shareef, M. S. Ahmed, A. Mohamed, and E. Al Hassan, “Review on home energy management system considering demand responses, smart technologies, and intelligent controllers,” IEEE Access, vol. 6, pp. 24498–24509, 2018.
  • [14] O. Ali and O. A. Mohammed, “Real-time co-simulation implementation for voltage and frequency regulation in standalone AC microgrid with communication network performance analysis across traffic variations,” Energies, vol. 17, no. 19, p. 4872, 2024.
  • [15] N. T. Mbungu, R. C. Bansal, R. M. Naidoo, M. W. Siti, A. A. Ismail, A. Elnady, and A. K. Hamid, “Performance analysis of different control models for smart demand–supply energy management system,” Journal of Energy Storage, vol. 90, p. 111809, 2024.
  • [16] K. Ukoba, K. O. Olatunji, E. Adeoye, T. C. Jen, and D. M. Madyira, “Optimizing renewable energy systems through artificial intelligence: Review and future prospects,” Energy & Environment, vol. 35, no. 7, pp. 3833–3879, 2024.
  • [17] A. R. Singh, R. S. Kumar, M. Bajaj, C. B. Khadse, and I. Zaitsev, “Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources,” Scientific Reports, vol. 14, no. 1, p. 19207, 2024.
  • [18] H. Wicaksono, M. Trat, A. Bashyal, T. Boroukhian, M. Felder, M. Ahrens, and T. Zoerner, “Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes,” The International Journal of Advanced Manufacturing Technology, vol. 138, no. 1, pp. 247–271, 2025.
  • [19] G. Indira, M. Bhavani, R. Brinda, and R. Zahira, “Electricity load demand prediction for microgrid energy management system using hybrid adaptive barnacle-mating optimizer with artificial neural network algorithm,” Energy Technology, vol. 12, no. 5, p. 2301091, 2024.
  • [20] M. M. H. Jin, S. H. Nengroo, J. Jin, D. Har, and S. Lee, “P2P power trading based on reinforcement learning for nanogrid clusters,” Expert Systems with Applications, vol. 255, p. 124759, 2024.
  • [21] N. Alfred, V. Guntreddi, A. N. Shuaibu, and M. S. Bakare, “A fuzzy logic based energy management model for solar PV–wind standalone with battery storage system,” Scientific Reports, vol. 15, p. 24660, 2025.
  • [22] O. Shahhoseini, “Microgrid and nanogrid implementation in smart cities,” in Sustainable Energy Resources in Smart Cities, pp. 175–202, 2025.
  • [23] Y. Yerasimou, M. Kynigos, V. Efthymiou, and G. E. Georghiou, “Design of a smart nanogrid for increasing energy efficiency of buildings,” Energies, vol. 14, no. 12, p. 3683, 2021.
  • [24] J. Li, Q. Luo, D. Mou, Y. Wei, P. Sun, X. Du, and M. Liserre, “Energy optimization management strategy for DC nano-grid cluster with high comprehensive energy efficiency,” IEEE Transactions on Smart Grid, vol. 14, no. 6, pp. 4439–4450, 2023.

Artificial Intelligence-Based Energy Management System Design in Nanogrid

Yıl 2026, Cilt: 16 Sayı: 1, 83 - 92, 31.01.2026

Öz

As energy independence and sustainability become more of a priority, nanogrid technologies are becoming more and more significant. These systenerji yönetim sistemleri aim to increase energy efficiency and reduce costs by optimizing local energy production, storage, and consumption. This article presents the design and simulation of an AI-based energy management system in nanogrids in the MATLAB/Simulink environment. The study examines the concept of nanogrids, their differences from microgrids, and the critical role of energy management systenerji yönetim sistemleri. The potential of AI techniques in energy management is emphasized, and the use of algorithms such as artificial neural networks (ANNs) is examined, particularly in areas such as load forecasting and battery management. The developed model was simulated using energy production, consumption, and storage data. The results were analyzed, and the advantages offered by AI-based energy management systenerji yönetim sistemleri over traditional methods were evaluated. Simulation findings demonstrate that the proposed AI-based approach can significantly improve the performance, reliability, and economic efficiency of nanogrid systenerji yönetim sistemleri.

Kaynakça

  • [1] M. Pipattanasomporn, M. Kuzlu, S. Rahman, "An Algorithm for Intelligent Home Energy Management and Demand Response Analysis," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2166-2173, 2012.
  • [2] S.A. Pourmousavi, M. H. Nehrir, "Real-Time Central Demand Response for Primary Frequency Regulation in Microgrids," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1988-1996, 2012.
  • [3] S. Parhizi, H. Lotfi, A. Khodaei, S. Bahramirad, "State of the Art in Research on Microgrids: A Review," IEEE Access, vol. 3, pp. 890-925, 2015.
  • [4] S.A.Arefifar, Y. A.-R. I. Mohamed, T. H. M. El-Fouly, "Comprehensive Operational Planning Framework for Self-Healing Control Actions in Smart Distribution Grids," IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4192-4200, 2013.
  • [5] M. E. Baran, I. El-Markaby, "A Multiagent-Based Dispatching Scheme for Distributed Generators for Voltage Support on Distribution Feeders," IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 52-59, 2007.
  • [6] S. Bahramirad, W. Reder, A. Khodaei, "Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2056-2062, 2012.
  • [7] M. Kuzlu, M. Pipattanasomporn, S. Rahman, "Review of communication technologies for smart homes/building applications," 2015 IEEE Innovative Smart Grid Technologies - Asia, pp. 1-6, 2015.
  • [8] M. R. Khan, Z. M. Haider, F. H. Malik, F. M. Almasoudi, K. S. S. Alatawi, M. S. Bhutta, “A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques,” Processes, vol. 12, no. 2, p. 270, 2024.
  • [9] M. A. Hannan, M. M. Hoque, A. Mohamed, A. Ayob, " Review of energy storage systems for electric vehicle applications: Issues and challenges," Renewable and Sustainable Energy Reviews, vol. 69, pp. 771-789, 2017.
  • [10] S. E. Eyimaya and N. Altin, “Review of energy management systems in microgrids,” Applied Sciences, vol. 14, no. 3, p. 1249, 2024.
  • [11] N. Einabadi and M. Kazerani, “Nanogrids in modern power systems: A comprehensive review,” Smart Cities, vol. 8, no. 1, p. 11, 2025.
  • [12] M.C.Di Piazza, “Volume II: Energy management systems for optimal operation of electrical micro/nanogrids,” Energies, vol. 17,p.1811, 2024.
  • [13] H. Shareef, M. S. Ahmed, A. Mohamed, and E. Al Hassan, “Review on home energy management system considering demand responses, smart technologies, and intelligent controllers,” IEEE Access, vol. 6, pp. 24498–24509, 2018.
  • [14] O. Ali and O. A. Mohammed, “Real-time co-simulation implementation for voltage and frequency regulation in standalone AC microgrid with communication network performance analysis across traffic variations,” Energies, vol. 17, no. 19, p. 4872, 2024.
  • [15] N. T. Mbungu, R. C. Bansal, R. M. Naidoo, M. W. Siti, A. A. Ismail, A. Elnady, and A. K. Hamid, “Performance analysis of different control models for smart demand–supply energy management system,” Journal of Energy Storage, vol. 90, p. 111809, 2024.
  • [16] K. Ukoba, K. O. Olatunji, E. Adeoye, T. C. Jen, and D. M. Madyira, “Optimizing renewable energy systems through artificial intelligence: Review and future prospects,” Energy & Environment, vol. 35, no. 7, pp. 3833–3879, 2024.
  • [17] A. R. Singh, R. S. Kumar, M. Bajaj, C. B. Khadse, and I. Zaitsev, “Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources,” Scientific Reports, vol. 14, no. 1, p. 19207, 2024.
  • [18] H. Wicaksono, M. Trat, A. Bashyal, T. Boroukhian, M. Felder, M. Ahrens, and T. Zoerner, “Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes,” The International Journal of Advanced Manufacturing Technology, vol. 138, no. 1, pp. 247–271, 2025.
  • [19] G. Indira, M. Bhavani, R. Brinda, and R. Zahira, “Electricity load demand prediction for microgrid energy management system using hybrid adaptive barnacle-mating optimizer with artificial neural network algorithm,” Energy Technology, vol. 12, no. 5, p. 2301091, 2024.
  • [20] M. M. H. Jin, S. H. Nengroo, J. Jin, D. Har, and S. Lee, “P2P power trading based on reinforcement learning for nanogrid clusters,” Expert Systems with Applications, vol. 255, p. 124759, 2024.
  • [21] N. Alfred, V. Guntreddi, A. N. Shuaibu, and M. S. Bakare, “A fuzzy logic based energy management model for solar PV–wind standalone with battery storage system,” Scientific Reports, vol. 15, p. 24660, 2025.
  • [22] O. Shahhoseini, “Microgrid and nanogrid implementation in smart cities,” in Sustainable Energy Resources in Smart Cities, pp. 175–202, 2025.
  • [23] Y. Yerasimou, M. Kynigos, V. Efthymiou, and G. E. Georghiou, “Design of a smart nanogrid for increasing energy efficiency of buildings,” Energies, vol. 14, no. 12, p. 3683, 2021.
  • [24] J. Li, Q. Luo, D. Mou, Y. Wei, P. Sun, X. Du, and M. Liserre, “Energy optimization management strategy for DC nano-grid cluster with high comprehensive energy efficiency,” IEEE Transactions on Smart Grid, vol. 14, no. 6, pp. 4439–4450, 2023.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Elektrik Enerjisi Üretimi (Yenilenebilir Kaynaklar Dahil, Fotovoltaikler Hariç), Elektrik Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Süleyman Emre Eyimaya

Gönderilme Tarihi 3 Ağustos 2025
Kabul Tarihi 23 Ocak 2026
Yayımlanma Tarihi 31 Ocak 2026
Yayımlandığı Sayı Yıl 2026 Cilt: 16 Sayı: 1

Kaynak Göster

APA Eyimaya, S. E. (2026). Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı. EMO Bilimsel Dergi, 16(1), 83-92. https://izlik.org/JA66NR48FK
AMA 1.Eyimaya SE. Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı. EMO Bilimsel Dergi. 2026;16(1):83-92. https://izlik.org/JA66NR48FK
Chicago Eyimaya, Süleyman Emre. 2026. “Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı”. EMO Bilimsel Dergi 16 (1): 83-92. https://izlik.org/JA66NR48FK.
EndNote Eyimaya SE (01 Ocak 2026) Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı. EMO Bilimsel Dergi 16 1 83–92.
IEEE [1]S. E. Eyimaya, “Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı”, EMO Bilimsel Dergi, c. 16, sy 1, ss. 83–92, Oca. 2026, [çevrimiçi]. Erişim adresi: https://izlik.org/JA66NR48FK
ISNAD Eyimaya, Süleyman Emre. “Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı”. EMO Bilimsel Dergi 16/1 (01 Ocak 2026): 83-92. https://izlik.org/JA66NR48FK.
JAMA 1.Eyimaya SE. Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı. EMO Bilimsel Dergi. 2026;16:83–92.
MLA Eyimaya, Süleyman Emre. “Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı”. EMO Bilimsel Dergi, c. 16, sy 1, Ocak 2026, ss. 83-92, https://izlik.org/JA66NR48FK.
Vancouver 1.Eyimaya SE. Nanoşebekede Yapay Zeka Tabanlı Enerji Yönetim Sistemi Tasarımı. EMO Bilimsel Dergi [Internet]. 01 Ocak 2026;16(1):83-92. Erişim adresi: https://izlik.org/JA66NR48FK

EMO BİLİMSEL DERGİ
Elektrik, Elektronik, Bilgisayar, Biyomedikal, Kontrol Mühendisliği Bilimsel Hakemli Dergisi
TMMOB ELEKTRİK MÜHENDİSLERİ ODASI 
IHLAMUR SOKAK NO:10 KIZILAY/ANKARA
TEL: +90 (312) 425 32 72 (PBX) - FAKS: +90 (312) 417 38 18
bilimseldergi@emo.org.tr