ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING MODELS FOR FORECASTING PROCESSES IN POWER SUPPLY SYSTEMS
Vladyslav Shevchenko- Oleksandr Shmatko
Опубліковано 31.10.2025
Як цитувати
Завантаження
Ця робота ліцензується відповідно до Creative Commons Attribution-ShareAlike 4.0 International License.
Анотація
Accurate forecasting of power supply parameters is one of the most fundamental tasks in modern energy system management [1]. The increasing penetration of renewable energy sources, the variability of consumer demand, and the transition to decentralized smart grids have introduced unprecedented complexity into the operation of power systems. Traditional forecasting methods, including statistical regression, time series analysis, and classical machine learning algorithms, often struggle to capture the nonlinear and transient dynamics of such systems.
Посилання
- 1. Mirzayev, S. N. O. G. L., & Esonov, T. B. O. G. L. (2024). Forecasting the urban electricity supply system. Oriental renaissance: Innovative, educational, natural and social sciences, 4(5), 558-563.
- 2. Küçükkara, M. Y., Atban, F., & Bayılmış, C. (2024). Quantum‐Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security. Advanced Quantum Technologies, 7(10), 2400084.
- 3. Hafeez, M. A., Munir, A., & Ullah, H. (2024). H-QNN: A hybrid quantum–classical neural network for improved binary image classification. AI, 5(3), 1462-1481.
- 4. Innan, N., Behera, B. K., Al-Kuwari, S., & Farouk, A. (2025). Qnn-vrcs: A quantum neural network for vehicle road cooperation systems. IEEE Transactions on Intelligent Transportation Systems.
- 5. Basha, R., Pathak, P., Sudha, M., Soumya, K. V., & Arockia Venice, J. (2025). Optimization of Quantum Dilated Convolutional Neural Networks: Image Recognition With Quantum Computing. Internet Technology Letters, 8(3), e70027.
- 6. Hong, Y. Y., Lopez, D. J. D., & Wang, Y. Y. (2024). Solar irradiance forecasting using a hybrid quantum neural network: A comparison on gpu-based workflow development platforms. IEEE Access, 12, 145079-145094.