# Load Forecasting with Machine Learning and Deep Learning Methods
## 5 Results
There does not seem to be a significant improvement in the XGBoost forecast by adjusting the weak learners, since RF and XGBoost show a similar tendency. Not considering the negative values, these have a dispersion that tends to overestimate at low demand levels and overestimate at high levels. This same tendency can be observed in Conv-1D. The LSTM adequately follows the trend of the real data, although it presents moments with greater dispersion, mainly in the residual demands. The dispersion and bias of the evaluated models in the test set is displayed in Figure 6. In contrast to what occurred in the validation set, the ML do not tend to falsify certain periods as missing values. The variation in the bias of the RF and XGBoost between the validation set (Figure 1) and test set (Figure 2), which changed from underestimate to overestimate, is due to this factor. As for the evaluation of the dispersion in the validation set, both models have a similar dispersion, without one model clearly standing out from the other. The SVR and the MLP have the same trend, with the MLP having more variance when it comes to predicting residual demands. When the electrical demand exceeds 1500 W, neither of these two models is able to follow the real data patterns. The Conv-1D has a behavior similar to MLP causalities, with an even greater tendency to overestimate in low demand periods. In the case of powers above the 1500 W range, unlike MLP, Conv-1D is capable of following the variations in demands with a certain dispersion. Except for residual demands, which LSTM tends to overestimate, this faithfully forecasts the patterns found in the real data. Figure 5: Relationship intensity in the adjustment of the models in the validation set. The medians, quartiles and IQR of the error assessment metrics evaluated, i.e., \(nMBE\), and \(nRMSE\), for the different models are displayed in Table 3. These values complement the figures presented throughout this section, reinforcing the comments with numerical values. The \(nMBE\) refers to the tendency of the models presented in Figures 1 and 2. \(nRMSE\) refers to the dispersion shown in Figures 3 and 4 for the validation and test sets, respectively, in each pair. Both metrics are represented in Figure 5 (validation set) and Figure 6 (test set), which display the degree of interrelation between the results obtained and the real values. Table 3 shows the stability of the models constructed by testing them in different folds. The IQRs that are presented in both \(nMBE\) and \(nRMSE\) in both splits show that the variability to the changes in the sets is reduced. Furthermore, looking at the changes in the median between the validation and the test set, it can be confirmed that the models are correctly fitted. In this way, knowing the test set as a set independent of training, i.e., where the quality of the modeling is evaluated under conditions different from those of the test, the reliability and reproducibility of the predictions made is guaranteed. Observing the values obtained for the forecast that are presented in Table 3, it can be verified that the LSTM is the model that best adapts to the electrical demands. If \(nRMSE\) is considered, the median is 2.76% in the validation set and 4.74% in the test set. The rest