# Prediction of Wind Power with Machine Learning Models
## 4 Conclusions  
Due to the inherent variability of wind power, forecasting presents a formidable challenge. Furthermore, successfully integrating wind power into primary power grids is a significant obstacle. As such, in this study, popular ML methods (ANN, RNN, CNN, and LSTM algorithms) with high predictive performance are examined to predict wind power. The algorithms' performances were assessed using statistical metrics, namely, MAE, RMSE, MSE, and R\({}^{2}\). Algorithms characterised by minimal errors indicated the most desirable and precise methodology. When the results presented above are examined, it can be understood that the proposed ML methods enable successful wind power estimation. To train and evaluate ML models, a SCADA system was employed to gather empirical data from January to December 2018, with a sampling frequency of 10 min. Additionally, the MERRA-2 Global data set, made available by NASA, was employed to evaluate the impact of meteorological data on wind power. When the correlation matrix between input parameters and active management is examined, it is observed that the strongest correlation among weather parameters is a correlation of 0.12 between cloud cover fraction and dynamic power. When analysing the correlation matrix, it becomes apparent that a notably robust correlation (0.95) exists between active power and the theoretical power curve. Then, it is observed that there is a correlation (0.91) between operational management and wind speed. The results showed that the LSTM, RNN, CNN, and ANN algorithms are powerful in forecasting wind power. Among these algorithms, LSTM is the best algorithm, with an R\({}^{2}\) value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614. DL models possess the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Machine** \\ **Learning Models** \\ \end{tabular} } & \multicolumn{3}{c}{**Performance Evaluation on Training Data Set**} & \multicolumn{3}{c}{**Performance Evaluation on Testing Data Set**} & \multicolumn{3}{c}{
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} } \\ \cline{2-10}  & **MAE** & **MSE** & **RMSE** & **R2** & **MAE** & **MSE** & **RMSE** & **R2** & \\ \hline ANN & 0.0224 & 0.0057 & 0.0757 & 0.9345 & 0.0245 & 0.0062 & 0.0787 & 0.9301 & 81.6 \\ CNN & 0.0218 & 0.0054 & 0.0732 & 0.9388 & 0.0235 & 0.0055 & 0.0742 & 0.9378 & 85.3 \\ RNN & 0.0196 & 0.0038 & 0.0615 & 0.9567 & 0.0218 & 0.0043 & 0.0656 & 0.9514 & 82.7 \\ LSTM & 0.0179 & 0.0027 & 0.0517 & 0.9694 & 0.0209 & 0.0038 & 0.0614 & 0.9574 & 85.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of models. ability to acquire intricate connections within data sets. The LSTM model utilised in this study stands out among deep learning models due to its capability to manage long-term dependencies effectively. As a result, LSTMs emerge as a valuable instrument for resolving issues involving time-dependent data, as exemplified by their application in wind power prediction. In applications like wind power forecasting, temporal changes over time are crucial. LSTMs can model complex relationships over time by preserving information from previous time steps, which enables them to be more accurate in predicting future wind power. This highlights LSTMs' valuable role in addressing challenges related to time-dependent data, as evidenced by their successful application in wind power prediction. Wind power forecasting is critical for energy production planning. The accurate predictions provided by LSTMs and similar deep learning models can enhance the efficiency of wind energy production scheduling. Based on these forecasts, energy companies can optimise their resource allocation and grid management. Accurate wind power predictions contribute to grid stability and reliability. Power grid operators can use these forecasts to balance energy supply and demand better, reducing the risk of blackouts and ensuring uninterrupted energy supply to consumers.