## 5 Experimental Results
### LightGBM Hyperparameter Optimisation  
An ideal model should make predictions with high accuracy and produce results as close as possible to the real values. Based on RMSE values, the best performers among the examined models are discussed in detail. In this context, it is discussed in detail that LightGBM performs well among the models examined, based on its RMSE values. Important points such as the advantages and limitations of LightGBM, the cases in which these models should be preferred, and the relationship between the characteristics of the dataset and model performance are emphasized. A regression model was created using the LightGBM algorithm, and cross-validation and hyperparameter tuning of this model were performed.  
Hyperparameter optimization was performed using GridSearchCV. The aim is to find the combination that gives the best performance by trying different combinations of the specified parameters, including 'n_estimators', 'learning_rate', and'max_depth'. In the LightGBM model, the 'n_estimators' parameter specifies the number of trees to be constructed within the model. Generally, a greater number of trees can enhance the model's complexity and potentially its performance; however, it also increases computational costs and the risk of overfitting. Therefore, it must be selected with care. The 'learning_rate' parameter governs the rate of learning in each iteration. A lower learning rate typically leads to slower learning and better generalization performance but may require more iterations to converge to a suitable solution. Conversely, a very high learning rate can cause the model to learn rapidly, yet it might also lead to overfitting. The'max_depth' parameter determines the maximum depth of a tree, that is, the maximum level of decision nodes. This parameter controls the complexity of the model and the level of detail in the relationships it can capture within the dataset. Deeper trees can model more complex relationships but again raise the risk of overfitting. The GridSearchCV method attempts all specified combinations  
Figure 8: RMSE results for machine learning models in kW.  
of these three hyperparameters and evaluates the model's performance for each combination through cross-validation. Afterward, a new LightGBM model was created using the best hyperparameters, and the performance of this model was measured again using cross-validation. The model was developed with the hyperparameters ('learning_rate': 0.01,'max_depth': 6, 'n_estimators': 500) producing the best results. The results obtained were compared with the RMSE scores of the initial model (Figure 9).  
The first column in the chart shows the RMSE of the initial LightGBM model. This value represents the initial performance of the model before adjusting the hyperparameter ('learning_rate': 0.1,'max_depth': 3, 'n_estimators': 100). The second column shows the final RMSE value obtained after hyperparameter tuning, which is approximately 190.34. Since the purpose of hyperparameter tuning is to improve the performance of the model, we can observe an improvement over the initial model in this case. This comparison shows that tuning the model's hyperparameters can significantly improve model performance, but it is important to note that this improvement may not always be successful. Furthermore, the practical significance of these changes must be evaluated in the context of a particular problem and depending on the characteristics of the dataset.