## 5 Results and Discussion
### Comparing the Results with the Literature  
The results obtained in this study were also compared with the findings of existing research to evaluate the performance of the proposed models and methodologies. Although this comparison provides valuable insights into the effectiveness of the advanced machine learning and statistical methods employed, it is important to note that the datasets used by different studies vary significantly in terms of time periods, market conditions, and geographic regions. These differences limit the potential for direct comparison, and any conclusions drawn should be interpreted with caution. The findings of this study indicate that the machine learning models, particularly XGBoost and ELM, achieved higher predictive accuracy compared to traditional statistical models like ARIMA and SARIMA. This observation is consistent with existing research, which often highlights the limitations of traditional models in capturing non-linear relationships and dealing with high levels of volatility inherent in electricity market data. For  
Figure 3: Comparison of time series-based methods. instance, [PERSON] et al. (2017) implemented ANN to predict day-ahead SMP in the Turkish electricity market and reported MAPE values ranging from 9.77% to 21.81%. In contrast, this study's XGBoost model combined with the MLFS consistently achieved lower MAPE values, such as 4.42% on 29 July 2023 and 5.87% on 19 May 2023. This significant reduction in error rates underscores the advantages of using ensemble learning methods like XGBoost, particularly when integrated with advanced feature selection techniques. However, it is essential to consider that [PERSON] et al. 's dataset may have included different temporal and market conditions, which could influence model performance and the comparability of results. Similarly, [PERSON] et al. (2017) [11] developed an ensemble learning model using AdaBoost, SVM, and Back Propagation Neural Networks (BPNNs), achieving MAPE values between 3.84% and 8.43%. The results of the current study indicate that XGBoost, when paired with MLFS, achieved comparable and even superior MAPE values, such as 4.42% on 29 July 2023, and 5.08% on 9 August 2023. This suggests that the inclusion of sophisticated feature selection techniques significantly reduces model complexity and enhances predictive accuracy. Nevertheless, the specific dataset characteristics and market dynamics considered by [PERSON] et al. may differ from those of the current study, affecting the generalizability of these findings across diverse market contexts. [PERSON] et al. (2019) [12] employed an ANN-based approach for day-ahead SMP forecasting and reported MAPE values as low as 5.36%. The proposed XGBoost and ELM models in this study achieved MAPE values below 5% on several occasions, such as 4.71% on 29 July 2023 and 5.11% on 9 August 2023, for XGBoost-MLFS. The improvement can be attributed to the integration of advanced feature selection methods like mRMR and MLFS, which refined the input variables and improved the model's ability to capture complex patterns in SMP data. However, [PERSON] et al. 's dataset likely covered a different set of variables and timeframes, making it difficult to establish a direct comparison. [PERSON] and [PERSON] (2019) [13] used FFNN for SMP forecasting in the Greek energy market, reporting MARNE values between 7.42% and 9.22%. The XGBoost and ELM models presented in this study achieved MAPE values significantly lower than this range (e.g., 4.71% on 29 July 2023, for XGBoost-MLFS). This reduction in error rates suggests that tree-based ensemble methods and ELMs are more adept at handling the complexities of SMP forecasting in highly dynamic electricity markets. Again, differences in the market structure and temporal scope of the datasets used in these studies should be considered when evaluating relative model performance. [PERSON] et al. (2023) [15] utilized XGBoost, RF, and LightGBM for SMP forecasting and achieved MAPE values as low as 2.70%.