## 4 Dataset and Methodology
### Model Training and Evaluation
The performance of each model was evaluated using MAPE, which is defined as follows:  
\[\text{MAPE}\ =\ \frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\widehat{y}_{i}}{y_ {i}}\right|\times 100 \tag{23}\]  
where \(y_{i}\) and \(\widehat{y}_{i}\) represent the actual and predicted values of SMP for the \(i\)-th observation, respectively, and \(n\) is the total number of observations. MAPE measures the average percentage error between actual and predicted values. This metric was chosen for its ability to effectively evaluate the accuracy of forecasts in the context of time series data with significant variability. All models were implemented in Python using libraries such as scikit-learn, TensorFlow, and XGBoost. The effectiveness of each model was assessed on a series of selected dates, representing different market conditions, including regular weekdays, weekends, and holidays. By analyzing model performance under these varying scenarios, the study aims to assess the adaptability and robustness of each forecasting approach. In more detail, 14 February 2023 represents a typical weekday with standard market activity and consistent electricity consumption patterns. This date was selected to establish a baseline for model performance under normal market conditions, serving as a reference point against which results from more irregular scenarios can be compared. In contrast, 25 March 2023 was chosen as a weekend day, characterized by decreased electricity demand due to reduced industrial and commercial activities. The inclusion of this date allows the study to analyze how models perform when faced with lower demand and reduced market volatility, common to weekends. The 19 May 2023 was selected as a public holiday, a day that often exhibits irregular consumption patterns and unique market behaviors. Public holidays typically see sharp drops in electricity consumption and altered generation schedules, making it essential to test the models' capability to handle these deviations from typical trends. Another date chosen to capture abnormal market behavior was 29 July 2023, which experienced high volatility and significant fluctuations in electricity prices. This date was used to assess how well the models adapt to rapid changes and non-linear interactions between variables, which are more challenging to predict. In contrast, 9 August 2023, was marked by relatively stable market conditions and minimal fluctuations, representing a low volatility day. The purpose of including this date was to evaluate the models' performance in scenarios where fewer abrupt changes occur, allowing them to leverage historical patterns effectively. The study further explored model consistency by selecting 30 August 2023, another public holiday. This date was chosen to compare with 19 May, assessing how the models handle different public holidays, each with its unique consumption patterns and potential market anomalies. Lastly, 14 September 2023 was included as a transitional period marking the end of the summer season. This date was used to test the models' ability to adapt to shifting seasonal dynamics and changing consumption trends, which often lead to gradual adjustments in electricity demand and supply patterns.