A Comparative Study of Machine Learning Techniques for Wind Turbine Performance Prediction

. The abstract describes a comparative study of various machine learning techniques for wind turbine performance prediction. The dataset used in this study is obtained from the National Renewable Energy Laboratory (NREL) and contains meteorological data and power output from a wind turbine. The machine learning techniques considered in this study include artificial neural networks (ANN), decision trees (DT), and random forests (RF). The results show that RF outperforms ANN and DT in terms of RMSE and MAE, while ANN outperforms DT and RF in terms of R-squared. Overall, this research demonstrates the effectiveness of machine learning techniques for wind turbine performance prediction and provides insights on the advantages and disadvantages of certain machine learning approaches. The findings of this research can be used to guide wind farm managers in selecting appropriate machine learning techniques for wind turbine performance prediction.


Introduction
Wind energy is one of the fastest-growing renewable energy sources, and wind turbines are vital in processing wind energy into electricity. Renewable energy sources have become increasingly important due to concerns about climate change and the depletion of fossil fuels [1] [13]. To optimize the performance of wind turbines and increase their efficiency, accurate prediction of wind turbine performance is essential [2] [14]. In past years, ML techniques have gained attention as effective tools for wind turbine performance prediction. However, there is a lack of comparative studies that evaluate the performance of these techniques in predicting wind turbine performance [3] [18]. This paper presents a similar using of ML models for wind turbine performance prediction, specifically focusing on the accuracy. The study is conducted using real-world wind turbine performance data, and the performance of each technique is measured and contrasted [4][5][6]. The results from this research can give additional insight for wind energy researchers and practitioners, as well as help in the development of more accurate and efficient wind turbine performance prediction models.

Related Work
Predicting Wind Turbine Power Output with Machine Learning Techniques explores the use of machine learning techniques for predicting wind turbine power output. The authors evaluate the accuracy of various models, including ANNs, SVMs, and RFR, and demonstrate that these models can provide accurate predictions of turbine power output [7] [12].
A comparison of machine learning techniques for wind turbine power curve modeling compares the performance of various machine learning techniques, including artificial neural networks, support vector machines, and decision trees, for wind turbine power curve modeling [8][9][10]. The study shows that ANN outperforms other techniques, highlighting the potential of neural network models for wind turbine performance prediction. Support Vector Machines for Wind Turbine Power Curve Estimation. This study explores the use of support vector machines (SVMs) for predicting wind turbine power output. The authors compare the performance of SVMs with other models and demonstrate that SVMs can provide accurate predictions of turbine power output [11] [15].
Wind Turbine Performance Prediction using Artificial Neural Networks. This study focuses on the use of artificial neural networks (ANNs) for predicting wind turbine performance [5] [16]. The authors evaluate the accuracy of ANNs in predicting turbine power output and compare their performance to other models. The study demonstrates that ANNs can be effective for wind turbine performance prediction.
Wind Turbine Power Prediction using Random Forest Regression evaluates the performance of random forest regression (RFR) for predicting wind turbine power output. The authors demonstrate that RFR can be effective for predicting turbine power output and outperforms other models in some cases [17] [19].

Wind Turbine performance Prediction
1. Data collection: The first step is to collect real-world wind turbine performance data from different sources. The data should include variables such as wind speed, direction, temperature, and power output. 2. Data preprocessing: Once the data is collected, it needs to be preprocessed to remove any missing or inconsistent data. The data may also need to be normalized or scaled to ensure that all variables are on the same scale. 3. Model development: Three machine learning techniques -artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs) -will be developed for predicting wind turbine performance. Each technique will be trained and tested using the preprocessed data. 4. Model evaluation: The performance of each model will be evaluated based on several metrics such as MAE, MSE, and R-squared. The models will also be compared based on their accuracy and efficiency. 5. Results analysis: The results of the study will be analyzed to determine which machine learning technique is the most accurate and efficient for predicting wind turbine performance. The findings will be presented in a clear and concise manner using tables, graphs, and other visual aids. 6. Conclusion and recommendations: The study will conclude with a summary of the findings and recommendations for future research. The limitations of the study will also be discussed. The wind turbine power formula (in Watts) is the coefficient of performance (efficiency factor, in percent),

Coefficient of Determination (R-squared)
One of the key formulas that can be used in a comparative study of machine techniques for wind turbine performance prediction is the Coefficient of Determination (R-squared): is the actual value of wind turbine performance for data point i. is the predicted value of wind turbine performance for data point i.

Figure 2.Comparison chart for R-squared
The figure 2 presents a comparison chart of R-squared values for existing ANN, SVM, and proposed WTPP algorithms. The x-axis means the dataset, while the y-axis denotes the Rsquared ratio.

Mean Absolute Error
One of the key formulas that can be used in a comparative study of machine techniques for wind turbine performance prediction is the MAE: = 1/ * | _ − | Where, n is the number of data points, _ is the actual output for the ℎ sample, is the predicted value of wind turbine performance for data point i.

Mean Squared Error
One of the key formulas that can be used in a comparative study of machine techniques for wind turbine performance prediction is the Mean Squared Error MSE): = 1/ * ( − ) 2 Where, n is the number of data points, is the actual value of wind turbine performance for data point i, is the predicted value of wind turbine performance for data point i.

Root Mean Squared Error
One of the key formulas that can be used in a comparative study of machine techniques for wind turbine performance prediction is the RMSE: = (1/ * ( − ) 2 ) Where, n is the number of data points, is the actual value of wind turbine performance for data point i, is the predicted value of wind turbine performance for data point i.

Conclusion
In this paper presented a comparative research of machine learning techniques for wind turbine performance prediction. The research involved collecting and preprocessing wind turbine performance data, developing and training three machine learning models (artificial neural networks, support vector machines, and random forests), and evaluating their performance using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R-squared). Overall, this study demonstrates the potential of machine learning techniques in predicting wind turbine performance, and provides insight into the comparative performance of different machine learning algorithms. The findings of this research could be useful for wind turbine operators and manufacturers in developing more accurate and efficient performance prediction models