Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts

• URL: http://arxiv.org/abs/2609.06194v1
• Date: Sat, 05 Sep 2026 17:33:34 GMT
• Title: Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
• Abstract summary: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns.<n> Accurate wind turbine power predictions can identify periods of low power that would be ideal for scheduling maintenance.<n>The effects of data volume, feature selection, and data preprocessing on the performance of such power prediction models have not been thoroughly studied.
• Score: 0.2740273306918099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 would be ideal for scheduling maintenance. However, the effects of data volume, feature selection, and data preprocessing on the performance of such power prediction models have not been thoroughly studied. Besides, current models have limited transferability between different wind turbines. Therefore, this study developed a baseline Linear Regression for performance comparison with a more complex Artificial Neural Network model to predict the power output of a wind turbine, using weather conditions only to enhance applicability. A range of data preprocessing techniques were studied, and models were trained on one month and one year of data to determine the effects of data preprocessing and volume on model performance. Feature selection was explored using a Random Forest Regressor. The best results from the different models showed that the Artificial Neural Network models provided the highest accuracy, with an R2 score of 0.98 and a low Mean Absolute Error of 194, when compared with the baseline model (R2 score of 0.94 and Mean Absolute Error of 441). The model performance is comparable to the range of results in past studies, with the advantage that the proposed method leverages a separate weather dataset from a nearby weather station, enabling future applications for similar wind turbines in different locations. The Artificial Neural Network model was then used to identify 4-h periods of low power predictions over 2 months (simulating application for future periods), providing power output savings of approximately 2000 kW for each maintenance event.

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