آمار

تعداد نشریات26
تعداد شماره‌ها306
تعداد مقالات2,643
تعداد مشاهده مقاله3,967,242
تعداد دریافت فایل اصل مقاله2,672,210
Talgatkyzy, D.S., Haroon, N.H., Hussein, S.A., Ibrahim, S.Kh., Jabbar, K.A., Mohammed, B.A., Hameed, S.M.. (1402). Renewable Energy Resources Development Effect on Electricity Price: an Application of Machine Learning Model. سامانه مدیریت نشریات علمی, 11(Special Issue (Open)), -. doi: 10.22098/joape.2023.13710.2049
D.S. Talgatkyzy; N.H. Haroon; S.A. Hussein; S.Kh. Ibrahim; K.A. Jabbar; B.A. Mohammed; S.M. Hameed. "Renewable Energy Resources Development Effect on Electricity Price: an Application of Machine Learning Model". سامانه مدیریت نشریات علمی, 11, Special Issue (Open), 1402, -. doi: 10.22098/joape.2023.13710.2049
Talgatkyzy, D.S., Haroon, N.H., Hussein, S.A., Ibrahim, S.Kh., Jabbar, K.A., Mohammed, B.A., Hameed, S.M.. (1402). 'Renewable Energy Resources Development Effect on Electricity Price: an Application of Machine Learning Model', سامانه مدیریت نشریات علمی, 11(Special Issue (Open)), pp. -. doi: 10.22098/joape.2023.13710.2049
Talgatkyzy, D.S., Haroon, N.H., Hussein, S.A., Ibrahim, S.Kh., Jabbar, K.A., Mohammed, B.A., Hameed, S.M.. Renewable Energy Resources Development Effect on Electricity Price: an Application of Machine Learning Model. سامانه مدیریت نشریات علمی, 1402; 11(Special Issue (Open)): -. doi: 10.22098/joape.2023.13710.2049

Renewable Energy Resources Development Effect on Electricity Price: an Application of Machine Learning Model

Journal of Operation and Automation in Power Engineering
دوره 11، Special Issue (Open)، خرداد 2023    اصل مقاله (1.17 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.13710.2049
نویسندگان
D.S. Talgatkyzy* 1؛ N.H. Haroon2؛ S.A. Hussein3؛ S.Kh. Ibrahim4؛ K.A. Jabbar5؛ B.A. Mohammed6؛ S.M. Hameed7
1Master of Science, Kazakh National Agrarian Research University, Abai Almaty, Kazakhstan
2Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq
3Department of Medical Laboratory Technics, Al-Manara College for Medical Sciences, Maysan, Iraq
4Department of Medical Laboratory Technics, Al-Nisour University College, Iraq
5Technical engineering college/ National University of Science and Technology, Dhi Qar, Iraq
6Department of Medical Engineering, Al-Hadi University College, Baghdad, Iraq
7Department of Optics, College of Health \& Medical Technology, Sawa University, Almuthana, Iraq
چکیده
Given the significant uncertainty surrounding future electricity prices, which is widely regarded as the most critical factor in this context, market participants must engage in forecasting to facilitate their exploitation and planning activities. The success of electricity market actors is dependent on the availability of more appropriate tools to address this issue. In contrast, there is a prediction of prices in the electricity market for varying periods due to the increasing use of renewable energy in global energy generation and the unsteady and disjointed configuration of renewable energy production. The fluctuating characteristics of wind energy production have increased the complexity of real-time demand management in power systems. This paper investigates the impact of renewable energy production on price forecasting using data from the Nord pool market's electricity market. The primary goal is to present a framework for forecasting market settlement prices using a hybrid wavelet-particle swarm optimization-artificial neural network (W-PSO-ANN). In two scenarios, the results showed that the proposed model accurately represents data and is more precise than the ANN and WANN models. Machine learning has demonstrated promise in predicting electricity prices, but it is not without limitations. The ANN, WANN, and W-PSO-ANN models have training phase RMSE indices of 0.09, 0.07, and 0.04 respectively. During testing, the values were 0.15, 0.11, and 0.08. This demonstrates that the proposed model outperforms previous models.
کلیدواژه‌ها
Renewable energy؛ electricity prices؛ electricity market؛ machine learning؛ wavelet؛ optimization model؛ artificial neural network؛ model performance
مراجع
  1. Tajfar, M. Pazoki, A. Pazoki, N. Nejatian, and M. Amiri, “Analysis of heating value of hydro-char produced by hydrothermal carbonization of cigarette butts,” Pollut., vol. 9, no. 3, pp. 1273–1280, 2023.
  2. Molajou, P. Pouladi, and A. Afshar, “Incorporating social system into water-food-energy nexus,” Water Resour. Manage., vol. 35, pp. 4561–4580, 2021.
  3. Ghasempour, M. A. Nazari, M. Ebrahimi, M. H. Ahmadi, and H. Hadiyanto, “Multi-criteria decision making (mcdm) approach for selecting solar plants site and technology: A review.,” Int. J. Renewable Energy Dev., vol. 8, no. 1, 2019.
  4. G. Abd-Elkader, D. F. Allam, and E. Tageldin, “Islanding detection method for dfig wind turbines using artificial neural networks,” Int. J. Electr. Power Energy Syst., vol. 62, pp. 335–343, 2014.
  5. Lowitzsch, C. E. Hoicka, and F. J. van Tulder, “Renewable energy communities under the 2019 european clean energy package–governance model for the energy clusters of the future?,” Renewable Sustainable Energy Rev., vol. 122, p. 109489, 2020.
  6. Rabbi, J. Popp, D. Máté, and S. Kovács, “Energy security and energy transition to achieve carbon neutrality. energ. 2022, 15, 8126.”
  7. Syed, C. Suresh, and S. Sivanagaraju, “Impact of renewable sources on electrical power system,” J. Oper. Autom Power Eng., 2023.
  8. Modu, M. P. Abdullah, A. L. Bukar, and M. F. Hamza, “A systematic review of hybrid renewable energy systems with hydrogen storage: Sizing, optimization, and energy management strategy,” Int. J. Hydrogen Energy, 2023.
  9. Operacz, A. Zachora-Buławska, I. Strzelecka, M. Buda, B. Bielec, K. Migdał, and T. Operacz, “The standard geothermal plant as an innovative combined renewable energy resources system: The case from south poland”, Energ., vol. 15, no. 17, p. 6398, 2022.
  10. S. A. M. K. Abad, P. Javidan, M. Baghdadi, and N. Mehrdadi, “Green synthesis of pd@ biochar using the extract and biochar of corn-husk wastes for electrochemical cr (vi) reduction in plating wastewater,” J. Environ. Chem. Eng., vol. 11, no. 3, p. 109911, 2023.
  11. Namvar and J. Salehi, “Adaptive residential energy hubs scheduling considering renewable sources,” J. Oper. Autom. Power Eng., vol. 12, no. 2, pp. 142–151, 2024.
  12. J. B. Kabeyi and O. A. Olanrewaju, “Sustainable energy transition for renewable and low carbon grid electricity generation and supply,” Front. Energy Res., vol. 9, p. 1032, 2022.
  13. E. Benti, G. S. Gurmesa, T. Argaw, A. B. Aneseyee, S. Gunta, G. B. Kassahun, G. S. Aga, and A. A. Asfaw, “The current status, challenges and prospects of using biomass energy in ethiopia,” Biotechnol. Biofuels, vol. 14, no. 1, pp. 1–24, 2021.
  14. Bali Swain, A. Karimu, and E. Gråd, “Sustainable development, renewable energy transformation and employment impact in the eu,” Int. J. Sustainable Dev. World Ecol., vol. 29, no. 8, pp. 695–708, 2022.
  15. Kasaeian, E. Bellos, A. Shamaeizadeh, and C. Tzivanidis, “Solar-driven polygeneration systems: Recent progress and outlook,” Appl. Energy, vol. 264, p. 114764, 2020.
  16. Tehranian, “Can machine learning catch economic recessions using economic and market sentiments?,” arXiv preprint arXiv:2308.16200, 2023.
  17. Ahmad, R. Madonski, D. Zhang, C. Huang, and A. Mujeeb, “Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm,” Renewable Sustainable Energy Rev., vol. 160, p. 112128, 2022.
  18. Zhu, H. Dong, W. Zheng, S. Li, Y. Huang, and L. Xi, “Review and prospect of data-driven techniques for load forecasting in integrated energy systems,” Appl. Energy, vol. 321, p. 119269, 2022.
  19. Tehranian, “Monetary policy & stock market,” arXiv preprint arXiv:2305.13930, 2023.
  20. Ghazvini, H. Maddah, R. Peymanfar, M. H. Ahmadi, and R. Kumar, “Experimental evaluation and artificial neural network modeling of thermal conductivity of water based nanofluid containing magnetic copper nanoparticles,” Physica A, vol. 551, p. 124127, 2020.
  21. Nourani, N. Rouzegari, A. Molajou, and A. H. Baghanam, “An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios,” J. Hydrol., vol. 587, p. 125018, 2020.
  22. P. Marugán, F. P. G. Márquez, J. M. P. Perez, and D. Ruiz-Hernández, “A survey of artificial neural network in wind energy systems,” Appl. energy, vol. 228, pp. 1822–1836, 2018.
  23. Sharghi, V. Nourani, H. Najafi, and A. Molajou, “Emotional ann (eann) and wavelet-ann (wann) approaches for markovian and seasonal based modeling of rainfall-runoff process,” Water Resour. Manage., vol. 32, pp. 3441–3456, 2018.
  24. -C. Chen, Z. Najat Rashid, B. Theruvil Sayed, A. Sari, A. Kateb Jumaah Al-Nussairi, M. Samiee-Zenoozian, M. Shokatian-Beiragh, et al., “Evaluation of hybrid soft computing models performance in estimating wave height,” Adv. Civ. Eng., vol. 2023, 2023.
  25. Nourani, A. Davanlou Tajbakhsh, A. Molajou, and H. Gokcekus, “Hybrid wavelet-m5 model tree for rainfallrunoff modeling,” J. Hydrol. Eng., vol. 24, no. 5, p. 04019012, 2019.
  26. Li, S. Miao, X. Luo, and J. Wang, “Optimization scheduling model based on source-load-energy storage coordination in power systems,” in 2016 22nd International Conference on Automation and Computing (ICAC), pp. 120–125, IEEE, 2016.
  27. Liu, Z. Deng, H. Wang, X. Zheng, X. Fu, and F. Wang, “Classified particle swarm optimization based algorithm for cooperative localization,” in China Satellite Navigation Conference (CSNC) 2020 Proceedings: Volume III, pp. 405–414, Springer, 2020.
  28. R. Kaloop, D. Kumar, F. Zarzoura, B. Roy, and J. W. Hu, “A wavelet-particle swarm optimization-extreme learning machine hybrid modeling for significant wave height prediction,” Ocean Eng., vol. 213, p. 107777, 2020.
  29. Golshani, M. Banan-Dallalian, M. ShokatianBeiragh, M. Samiee-Zenoozian, and S. Sadeghi-Esfahlani, “Investigation of waves generated by tropical cyclone kyarr in the arabian sea: An application of era5 reanalysis wind data,” Atmos., vol. 13, no. 11, p. 1914, 2022.
  30. Chang, Y. Zhang, and W. Chen, “Electricity price prediction based on hybrid model of adam optimized lstm neural network and wavelet transform,” Energy, vol. 187, p. 115804, 2019.
آمار
تعداد مشاهده مقاله: 131
تعداد دریافت فایل اصل مقاله: 104


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب