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AI models predict wind turbine power for optimized maintenance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed and compared machine learning models for predicting wind turbine power output, aiming to optimize maintenance scheduling. An Artificial Neural Network model achieved a high accuracy with an R2 score of 0.98 and a Mean Absolute Error of 194, outperforming a baseline Linear Regression model. The study also explored feature selection using a Random Forest Regressor and found that using a separate weather dataset enhances the model's applicability to different wind turbines and locations. The developed Artificial Neural Network model can identify low-power periods, potentially saving significant energy during maintenance events. AI

IMPACT Enhances efficiency in renewable energy by optimizing maintenance schedules for wind turbines through accurate power prediction.

RANK_REASON Academic paper detailing a novel application of machine learning models for a specific engineering problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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AI models predict wind turbine power for optimized maintenance

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Academic paper detailing a novel application of machine learning models for a specific engineering problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Khivishta Boodhoo, Isaac Triguero, Josh Plumbly, Bruce Nicolson, Nicholas Watson ·

    Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts

    arXiv:2609.06194v1 Announce Type: new Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can identify periods of low power that wo…

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