## 4 Discussion  
Although one of the most common machines used for load demand forecasting is LSTM neural networks, as shown in the literature, what sets the different works apart is the training process. The information used for training is crucial for achieving accurate forecasting results. The more suitable the information, the better the outcome will be.  
Separating load profiles into clearly defined clusters reveals distinct operational regimes within the energy demand data. Therefore, clustering provides valuable insights into the underlying factors influencing these regimes, such as weather conditions, economic activities, or social behaviors. Understanding these patterns is essential for utilities and grid operators, as it enables more targeted and efficient demand management strategies. Moreover, the clusters reflect different energy consumption levels and illustrate the temporal structure of demand throughout the day. This refined understanding of the load profiles enhances the predictive capabilities of models and supports better decision-making processes in energy management. Furthermore, the similar-day methodology, in its various forms, is a widely used method for day-ahead load demand forecasting. This approach, which combines the technique of distinguishing between weekdays and weekends with clustering analysis, significantly enhances the relevant training information for the LSTM neural network.  
Load demand is significantly influenced by weather conditions, with temperature being one of the most important factors. Therefore, it is essential to incorporate this information to achieve accurate forecasting results. A critical challenge in regional load forecasting arises when considering meteorological conditions across the entire region of interest, especially if it is a large area with varying local weather patterns. Thus, utilizing representative meteorological data by calculating a weighted average from the sites with higher loads in the region, as proposed in this approach, offers a solution to the challenge of integrating diverse climate conditions within a region.  
Even though it was demonstrated that the proposed methodology yields better results than CENACE, it is valuable to compare it with methodologies employing similar paradigms. Research was undertaken on load demand forecasting for very short-, short-, medium-, and long-term load demands using LSTM neural networks. In particular, some of the most recent studies published on day-ahead load demand forecasting utilize LSTM neural networks as the primary machine-learning tool for achieving accurate predictions, as these networks are designed to capture long-term dependencies in sequential data. Table 6 summarizes recent research that employs this recurrent network, either independently or in conjunction with another tool, for day-ahead load demand forecasting alongside the proposed approach presented.  
As seen in Table 9, the main characteristics that differentiate the proposed day-ahead regional load forecasting methodology are as follows:  
* It is used for regional load forecasting, regardless of the extension of the region.
* It defines and uses representative regional weather data, regardless of the extension of the region.
* It can achieve better accuracy in its forecasting results, as shown by the MAPE values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Week** & **MAPE** & **MAPE\_C** \\ \hline Easter & 1.25\% & 2.49\% \\ Maximum load demand & 1.15\% & 1.17\% \\ Minimum load demand & 1.14\% & 2.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Historical load demand data and forecasted load demand values obtained using our methodology and CENACE’s methodologies for the three weeks under study.  
* Its methodology is less complex compared with the other regional load forecasting methodologies.