## 3 Results
### Case Study
#### 3.1.5 Results of Proposed Day-Ahead Load Forecasting Methodology with Real Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **H1 Neurons** & **H2 Neurons** & \
\begin{tabular}{c} **H1 Activation** \\ **Function** \\ \end{tabular} &
\begin{tabular}{c} **H2 Activation** \\ **Function** \\ \end{tabular} \\ \hline LSTM—Cluster-based training & 64 & 32 & Hyperbolic tangent & Linear \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Architecture of the LSTM model. Neurons and activation functions of hidden layer 1 (H1) and hidden layer 2 (H2). Figure 11: Historical and forecasted load demand data for a day in the winter season corresponding to (**a**) Thursday 13 January 2022 and (**b**) Sunday 16 January 2022. Figure 12: Historical and forecasted load demand data for a day in the spring season corresponding to (**a**) Thursday 21 April 2022 and (**b**) Sunday 24 April 2022. The results obtained for both types of days in this season indicate that the prediction is generally accurate, except during significant shifts in the demand trend, such as peaks. Figure 13a illustrates the load demand and forecasted values for the summer season. 14 July 2022 is a Thursday. The load demand begins at approximately 6800 MWh, decreases to nearly 6000 MWh/h by 5 PM, and then rises again to 6800 MWh/h by 3 PM, maintaining that level until 6 PM. Afterward, there is a swift increase to nearly 7400 MWh/h around 9 PM, followed by another decline. In comparison, Figure 13b shows the load demand and forecasted values for 17 July 2022, which is a Sunday. As observed, the pattern is quite similar to that in Figure 11a. Load demand starts from a slightly lower value than on the weekday, at nearly 6750 MWh/h, decreases to 5500 MWh/h by 7 AM, and then begins to rise steadily to nearly 6500 MWh/h. It shifts to 6250 MWh/h around 5 PM before showing a sharp increase to reach 7500 MWh/h around 9 PM before decreasing again. The summer season coincides with the rainy season in the region and is milder than the previous season, leading to lower energy consumption compared to spring. This season's forecasting results appear promising, highlighting a small discrepancy in the peak values. Figure 14 illustrates energy consumption along with the forecasted values for autumn days, specifically 6 and 9 October 2022. The load patterns for weekdays and weekends closely resemble those of previous seasons, showing variations in both maximum and minimum load demand values. The minimum load demand is approximately 5600 MWh/h for a weekday and 5250 MWh/h for a weekend day, as shown in Figures 14a and 14b, respectively. Similarly, the maximum load demand values approach nearly 7000 MWh/h and 6750 MWh/h for Thursday and Sunday, as indicated in Figures 14a and 14b, respectively. Once again, the predicted trend in load demand mirrors the actual data, closely aligning  
Figure 13: Historical and forecasted load demand data for a day in the summer season corresponding to (**a**) Thursday 14 July 2022 and (**b**) Sunday 17 July 2022.  
with real behavior during smooth changes in the trend and deviating when the changes are more abrupt. Figure 15 shows the energy consumption values registered on 21 May 2022, which corresponds to the day with maximum load demand during the year. Finally, Figure 16 presents the forecasting results for a holiday on 25 December 2022. The prediction is successful for most of the day, with a slight deviation around 6 AM. The quantification of these forecasting results will be detailed in the next subsection. Figure 14: Historical and forecasted load demand data for a day in the autumn season corresponding to (**a**) Thursday 6 October 2022 and (**b**) Sunday 9 October 2022. Figure 15: Historical and forecasted load demand data for the day with maximum load demand corresponding to 21 May 2022. These findings emphasize the importance of using a clustering approach in our methodology.