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

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...

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Bibliographic Details
Published in:Energies (Basel) 2023-10, Vol.16 (19), p.6889
Main Authors: Safari, Ashkan, Kheirandish Gharehbagh, Hamed, Nazari Heris, Morteza
Format: Article
Language:English
Subjects:
Accuracy
Air-turbines
Algorithms
Alternative energy sources
Artificial intelligence
Data mining
Deep learning
Efficiency
Electric power production
Energy consumption
Force and energy
gradient boosting regressor
Green technology
INVELOX wind turbine
Lunar eclipses
Machine learning
Neural networks
Optimization
Parameter estimation
power forecasting
renewable production
Renewable resources
simulation/data-driven prediction
Systems stability
Turbines
Velocity
Weather forecasting
Wind power
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16196889