# Prediction of Wind Power with Machine Learning Models
## 2 Materials and Methods
### Proposed Model Architecture  
The predictive performance of the intended models was evaluated using two data sets. The first data set pertains to internal wind turbine data, while the second comprises external weather-related data. These two data sets were merged to create a final data set with 8760 observations. The initial data set consisted of 50,530 samples, with 2030 missing data instances identified. The missing data was successfully imputed using the kNN algorithm to address this. Following this imputation, a new data set was generated by filtering only the hourly data points. After completing the data pre-processing steps, the data set was split into 75% for training and 25% for testing, using a random state value of 42. To maintain consistency among the data set's features, the training and testing data were normalised using the min-max scaling method. During the training process, the parameter optimisation was carried out using the Adam (Adaptive Moment Estimation) algorithm, with an initial learning rate value set to 0.001. The MSE was employed as the loss function between the input and output. For each model, the number of epochs was developed to 100, and the batch size was 32. Subsequently, utilising these parameters, four different state-of-the-art and popular DL neural network architectures were compared to identify the optimal model for wind power estimation. Wind power was estimated using ANN, CNN, RNN, and LSTM methods using meteorological and turbine characteristic data.  
Figure 6 represents a flowchart of the intended prediction model. In the study, the first model employs an ANN-based approach, the second model utilises a CNN-based DL architecture known for its success in large data sets, the third model incorporates an RNN architecture that is effective for time series data, and the fourth model employs an LSTM model, which yields successful results in analysing time series with more extended time intervals or complex structures. The objective is to identify the optimal hyperparameter combination that maximises model performance. To achieve this, a randomised search was employed, testing numerous hyperparameter settings to identify those that yielded the best performance.  
Figure 5: Theoretical power curve and active power.  
Among ML methods, ANNs, RNNs, CNNs, and LSTMs are important in wind power forecasting. The ANN model is known for its ability to learn complex relationships and is useful for discovering patterns within large data sets. It also has a flexible structure and is effective for general predictions. CNNs are particularly successful in visual data processing but can also work well with time-series data. It is suitable for processing multidimensional data, such as wind speed and direction. RNNs are designed for processing time-series data and maintain information from previous time steps. This makes them a suitable choice for modeling and forecasting.  
Figure 6: Flowchart of prediction model.  
modelling changes over time. LSTMs are an improved version of RNNs and can handle long-term dependencies. They are well-suited for modelling various effects over time in wind power prediction.