## 6 Discussion  
The model selection process is particularly difficult in wind energy forecasting. The variable nature of wind and the complexity of datasets make it difficult for models to accurately reflect real-world data. Challenges include ensuring models accurately predict factors such as highly variable wind speed and direction. In addition, it is necessary to select appropriate hyperparameters and maintain the generalizability of the model by avoiding situations such as overfitting or underfitting. The difficulties that may be encountered in this process have been reduced thanks to the careful work carried out in the data preprocessing step. Steps such as model testing and hyperparameter tuning ensure that the correct model is selected, or the selected model is improved. Hyperparameter adjustments of the LightGBM model played an important role in maximizing the performance of the model on the data. These adjustments helped the model avoid problems such as overfitting or underfitting, and improve prediction accuracy. Deploying the model using Docker ensured that the model worked consistently in different environments. Dependency management is simplified, and deployment of the model is accelerated. These strategies were implemented by performing hyperparameter optimization using GridSearchCV and packaging and deploying the model in a Docker container. When these two strategies are combined, the overall performance of the model is increased, ensuring application flexibility and deployment efficiency. Although the integration of machine learning and MDPs has significantly accelerated model development and deployment processes, some limitations have been encountered. These limitations include technical and operational challenges, high startup costs (for researchers using licensed software infrastructure), expertise and training requirements, managing model updates, and difficulties in ensuring consistency across different environments. In addition, difficulties in maintaining the generalization ability of the model in complex datasets and real-world scenarios are also important. However, MDPs overcomes technical and operational challenges and has allowed model updates to be managed more regularly and effectively. Therefore, using MDPs offers the benefits of managing complexity and facilitating collaboration while increasing the speed, reliability, and scalability of machine learning projects. Table 2 shows the comparison of the proposed model with previous studies. However, the data, methods, and success rates obtained varied from study to study. In Table 2, the performance of various regression and classification algorithms reported in the literature is evaluated. The referenced studies [15; 22; 35; 36; 37; 38] focus on developing novel time series forecasting methods. [PERSON] et al. integrated the LightGBM model with four traditional meteorological features as inputs and computed an RMSE value. To improve the outcomes, they combined the LightGBM model with the Mutual Information Coefficient (MIC). This enhanced model, when fed with eleven meteorological features (such as gravity wave stress and heat flux), showed a 24% improvement in RMSE. These results indicate that the selection of meteorological data by MIC significantly enhances prediction accuracy [35]. [PERSON] et al. focused on two types of GBM prediction models, considering the seasonal nature of wind. One model included a training set with data from July, matching the test set's month. The other model's training set consisted of data from the same season as the