## 5 Conclusions  
The proposed methodology proved effective in providing day-ahead regional load demand forecasts with an acceptable error margin. In all forecasted scenarios, the mean absolute percentage error (MAPE) was below the acceptable threshold for forecasting performance in CENACE, which is 4%; indeed, all computed MAPEs were below 1.5%. For the various scenarios analyzed, on most days and during most hours, the load demand forecasts generated by the proposed methodology were more accurate than those forecasted by CENACE. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Approach** & **Load Dataset** & **Input Variables** & **Accuracy of Prediction** \\ \hline [PERSON] et al., 2017 [52] & LSTM & A set of households in Australia & Historical load time series & MAPE values ranging from 21.99\% to 26.85\%. \\ [PERSON] et al., 2018 [53] & LSTM + RNN + GA & Metropolitan zone in France & Historical load time series & CV(RMSE) = 0.78\%. \\ [PERSON] et al., 2020 [54] & LSTM & Industry in Italy & Historical load time series & nRMSE = 2.30. \\ [PERSON] et al., 2021 [55] & Bi-LSTM S2S & Residential region in Pakistan & Historical load time series & MAPE values ranging from 3.67\% to 5.92\%. \\  & & Residence, hotel, manufacturing plant, EV charging station, distribution network, transmission network in USA and Italy & They obtain RMSE values lower than the benchmark models, ranging from \(-6.3\%\) to 73\% improvement over the benchmarks. \\ [PERSON] et al., 2020 [57] & Uni-LSTM + Bi-LSTM & Power system network in Switzerland load & Historical load ranging from 0.22\% to 3.895\%. \\ [PERSON] et al., 2023 [51] & LSTM & Power system network in and GRCORI & Historical load time series & MAPE values ranging from 0.36\% to 3.58\%. \\  & & Historical load and & MAPE values ranging from 0.22\% to 3.895\%. \\ [PERSON] et al., 2024 [28] & LSTM + DWT + NARX + SVM & Power system network in Germany and Iran & Historical load and & MAPE values ranging from 0.29\% to 4.2\%. \\  & & & social events & MAPE values ranging from 0.63\% to 1.48\%. \\ This approach & LSTM + K-means clustering + similar days & Power system network in GRCORI & Historical load meteorological time series & MAPE values ranging from 0.63\% to 1.48\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Some of the day-ahead load forecasting methodologies utilizing LSTM neural networks reported in the literature. The presented methodology was demonstrated over three interesting weeks due to the challenges in forecasting periods of maximum and minimum load demand, as well as periods of high variability in the load levels, such as those reported during Easter week. These periods correspond to the most difficult operating scenarios and thus deserve special attention from both GCRORI and, in general, all power system operators. The enhanced accuracy achieved in load prediction using the proposed day-ahead load forecasting model is particularly significant under complex operating conditions. For example, during peak load demand periods, power system elements often need to operate at or even exceed their nominal capacities, especially power transformers and transmission lines. Such conditions can lead to disturbances in the system, potentially resulting in significant blackouts. Conversely, during low peak load demand periods, certain power system elements, such as transmission lines and compensation devices like STATCOMs and static VAR compensators, as well as shunt capacitors and reactors, must be employed to maintain nodal voltages within secure ranges. In a longitudinal power grid, like that of GCRORI, the power transmission lines operate below their surge impedance loading (SIL). Consequently, they behave like shunt capacitors, injecting reactive power (Q) into the grid and raising nodal voltages. Therefore, in this minimum peak load scenario, the power transmission lines often need to be disconnected.