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DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm

Author

Listed:
  • Ashkan Safari

    (Faculty of Electrical, and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran)

  • Hamed Kheirandish Gharehbagh

    (Faculty of Electrical, and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran)

  • Morteza Nazari Heris

    (College of Engineering, Lawrence Technological University, Southfield, MI 48075, USA)

Abstract

The transition to sustainable electricity generation depends heavily on renewable energy sources, particularly wind power. Making precise forecasts, which calls for clever predictive controllers, is a crucial aspect of maximizing the efficiency of wind turbines. This study presents DeepVELOX, a new methodology. With this method, sophisticated machine learning methods are smoothly incorporated into wind power systems. The Increased Velocity (IN-VELOX) wind turbine framework combines the Gradient Boosting Regressor (GBR) with the Grey Wolf Optimization (GWO) algorithm. Predictive capabilities are entering a new age thanks to this integration. This research presents DeepVELOX, its structure, and results. In particular, this study presents the considerable performance of DeepVELOX. With a MAPE of 0.0002 and an RMSPE of 0.0974, it gets outstanding Key Performance Indicator (KPI) results. The criteria of Accuracy, F1-Score, R2-Score, Precision, and Recall, with a value of 1, further emphasize its performance. The result of this process is an MSE of 0.0352. The significant reduction in forecast disparities is made possible by this system’s remarkable accuracy. Along with improving accuracy, the integration of machine learning algorithms, including GBR, the GWO algorithm, and wind turbine operations, offer a dynamic framework for maximizing power and energy capture.

Suggested Citation

  • Ashkan Safari & Hamed Kheirandish Gharehbagh & Morteza Nazari Heris, 2023. "DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm," Energies, MDPI, vol. 16(19), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6889-:d:1250986
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    References listed on IDEAS

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    1. Mahsa Dehghan Manshadi & Milad Mousavi & M. Soltani & Amir Mosavi & Levente Kovacs, 2022. "Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System," Energies, MDPI, vol. 15(24), pages 1-16, December.
    2. Kamani, D. & Ardehali, M.M., 2023. "Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources," Energy, Elsevier, vol. 268(C).
    3. Ghorani, Mohammad Mahdi & Karimi, Behrooz & Mirghavami, Seyed Mohammad & Saboohi, Zoheir, 2023. "A numerical study on the feasibility of electricity production using an optimized wind delivery system (Invelox) integrated with a Horizontal axis wind turbine (HAWT)," Energy, Elsevier, vol. 268(C).
    4. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    5. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    6. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    7. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.
    8. Milad Shojaee & Fatemeh Mohammadi Shakiba & S. Mohsen Azizi, 2022. "Decentralized Model-Predictive Control of a Coupled Wind Turbine and Diesel Engine Generator System," Energies, MDPI, vol. 15(9), pages 1-13, May.
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