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

Ashkan Safari, Hamed Kheirandish Gharehbagh and Morteza Nazari Heris ()
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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

Energies, 2023, vol. 16, issue 19, 1-22

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.

Keywords: artificial intelligence; gradient boosting regressor; INVELOX wind turbine; renewable production; power forecasting; simulation/data-driven prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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