## 3 Results and Discussion
### Model Assessments  
Several performance indicators were employed to investigate the reliability of the forecasting of the used models. These indicators are discussed in this section to assess the accuracy and generalization of the used models. Five accuracy performance statistical metrics were employed in this study. These metrics include MAPE, MAE, RMSE, CVRMSE, and R\({}^{2}\). Each of the metrics shed light on one angle of the accuracy of the data. MAPE can be considered one of the utmost used tools for evaluating the accuracy of models. MAE reveals the difference between the estimated value and the real value using the absolute error. RMSE assesses the variability of model response regarding variance and sensitivity to large errors. CVRMSE standardizes the forecasted error and provides a unit-less metric that evaluates the variability of the errors between predicted and real values. Table 3 presents the calculated values of various statistical performance metrics (MAPE, MAE, RMSE, CVRMSE, and R\({}^{2}\)) for the used models. Five different models were used: The single Prophet, multiple Prophet models, single, double, and triple seasonality Holt-Winters models. Abbreviations are used to denote the different models used: PSR denotes a Prophet single regressor model. PMR denotes Prophet multiple regressors model, HWSS denotes Holt-Winters with single seasonality model, HWDS denotes Holt-Winters with double seasonality model, and HWTS denotes Holt-Winters with triple seasonality model. The triple seasonality Holt-Winters model achieved a superior value of accuracy in comparison with other model's performance metrics. The double seasonality Holt-Winters model was the second with all performance measures comparable to the triple seasonality model. MAPE was proposed as a reference indicator for assessing energy forecasting performance at different horizons [2]. All models achieved a low MAPE value of less than 5%, which indicates the models' high accuracy. It is benchmarked [40] that When MAPE is less than 10%, it is considered a highly accurate model. Highly accurate models were then categorized into four levels, with the best model denoted by I level when MAPE is \(\leq\)1.2%. Other levels are as follows: II (1.2-2.8%), III (2.8-4.6%), and IV (4.6-10%) [2]. Three of the used models, namely the multiple Prophet model and the double and triple seasonality of Holt-Winters models, fall in the II level of this rating, whereas the other two, namely single Prophet and single seasonality of Holt-Winters model, fall in the III level. The triple seasonality Holt-Winters model outperforms all the other models in MAPE value. Superiority is also observed for both double and triple seasonality Holt-Winters models in MAPE values compared to other models. The MAPE values were 1.76% and 1.83% for triple and double seasonality Holt-Winters models, respectively. The coefficients of determination, R\({}^{2}\), of the five models are plotted with excellent values in Figure 3, showing small differences among the forecasted and real data values. The best-fitting was for the Holt-Winters model's triple seasonality with R\({}^{2}\) = 0.9899, then to the Holt-Winters model's double seasonality R\({}^{2}\) = 0.9893 followed by multiple Prophet model with R\({}^{2}\) = 0.9743. The least values of R\({}^{2}\) were 0.9709 and 0.9641 for the single Prophet model and single seasonality of the Holt-Winters model, respectively. The MAE of the best model, which is the triple seasonality Holt-Winters, was found to be 47.82, whereas the value of MAE for the least model, which was the single seasonality Holt-Winters model, was 127.82, which is approximately three times of the triple model.