# Load Forecasting with Machine Learning and Deep Learning Methods
## 5 Results
The model with the greatest dispersion is the Conv-1D, with an IQR of 1.93%. The median of this model is twice the value presented in the validation, growing from 0.48% to 1.59%. In this case, there is greater variation in the adjustment. The rest of the models have a variation from the median of about \(\pm 0.5\%\). This affects the bias in the XGBoost and RF predictions, which change from underestimating to overestimating, and MLP, which changes to underestimating. The bias in the rest of the models remains stable, repeating the trend that occurs in the training. The most stable model is LSTM, which despite presenting a slightly worse fit, reduces the median by 0.51% and maintains the bias, i.e., continues to overestimate. It has an IQR similar to that of the training, which is 0.8% higher in the testing. Figure 1: Bias in the validation set of the different models using k-fold method. Figure 2: Bias in the test set of the different models using k-fold method. Figure 3 shows the variance in the predictions in the validation set from the calculation of the \(nRMSE\). The models with less divergence in the results are SVR and MLP, with IQR values of 0.57% and 0.43%, respectively. Nevertheless, these are the models that present the worst adjustments, with the median being 13.80% and 14.32%, respectively. In addition, both models present values above and below the ranges established as acceptable. Although RF and XGBoost improve the variance of the previously presented models and the medians are 9.44% and 9.33%, respectively, they also present anomalous values. In contrast, neither the Conv-1D nor the LSTM present anomalous values. The IQR of both models is similar: 1.76% for Conv-1D, and 1.71% for LSTM. The variance, based on its median value, is 9.64% and 2.76%, respectively. Thus, LSTM is shown to be the model with lower variance. The variance in the forecasts in the test set are shown in Figure 4. The variances of the SVR, MLP, and Conv-1D are the highest, with their medians being 14.62%, 15.59%, and 14.00%, respectively. However, these are models with less dispersion in the results, with IQR values of 0.09% for SVR, 0.06% for MLP, and 0.36% for Conv-1D. The dispersion in all the models in the test set is less than that presented in the validation set. The largest reduction in IQR occurs for RF, with a decrease of 1.87%. In contrast, MLP has the least reduction, with a decrease in the IQR of 0.37%. RF and XGBoost are the only models that have decreased variance with respect to validation, with 1.69% and 1.74%, respectively. The other models have increases, with the greatest variation for Conv-1D (4.36%), and the lowest for SVR (0.82%). RF, XGBoost, and MLP continue to have outliers. Nevertheless, the SVR establishes the values in the accepted range, and the Conv-1D starts presenting 1 anomalous value. As in the validation set, the LSTM is the model with the lowest variance, with a value of 4.74%. Figure 4: Variance in the test set of the different models using k-fold method. Figure 3: Variance in the validation set of the different models using k-fold method. Figure 5 shows the dispersion and tendency of the forecast with respect to the actual values in the validation set. In contrast to the DL techniques, which are in the positive range, the ML architectures include instants in which the predictions are negative. This is because the validation set contains gaps to ensure the data continuity. These suffer from a slight inertia in the first learning epochs and are not capable of following the information provided by the data at the instants of change in which the signal is recovered. In general, a tendency can be observed for all models to overestimate low demand and underestimate high demand. The MLP and SVR show a similar trend. Up to 1500 W, they follow the patterns of the real data with a clear inclination to overestimate. Thereafter, they do not follow the trend and underestimate the electrical loads.