# Load Forecasting with Machine Learning and Deep Learning Methods
## 6 Discussion  
The substantial impact of the building sector on emissions of greenhouse gases means that adopting measures to meet sustainable development objectives is vital. The implementation of techniques to characterize fluctuations in the electrical loads enables better management of the energy needs in existing buildings. Thus, the comparative analysis of different AI techniques carried out in this paper provides a better understanding of the most appropriate models for forecasting electric power demand. The effective implementation of these techniques enables a more efficient and reliable energy system in buildings and can guide research in the field of ZEB. Obtaining accurate electrical energy forecast models is key in the development of the building sector. Greater knowledge of electrical loads directly contributes to better use of energy since it allows greater control and a better response to the needs of buildings. The improvement in energy efficiency encourages facilities to adopt the necessary measures and facilitate the transition to the ZEB. Therefore, it offers the possibility of meeting the objectives set at a global level for reducing emissions. In this paper, the models are constructed with missing values retained to preserve the continuity of the data. To compare results effectively, a seed is used. The performance of the models is obtained by analyzing their bias and variance in the validation and test samples. The reliability of the models is determined based on their ability to predict within a low \(IQR\) from different sets obtained by a cross-validation technique. The adjustment that occurs between the predictions in the validation and test sets ensures the capability of the models to reproduce the results and their ability to forecast electrical loads. There is not a linear relationship between the inputs considered to predict the electric energy since models with a more basic pattern extraction, such as MLP, fail to predict the objective variable. The inability shown by the SVR and MLP to predict variations in demand makes it impossible to use these models for the prediction of electricity consumption in buildings. All the models, except the LSTM, which has a slight tendency to overestimate the real values, show underestimation. In the case of adding human reasoning in post-processing, i.e., by invalidating the predictions with negative models of the ML values, it can be ensured that the RF and XGBoost follow the electrical patterns produced with a  
Figure 8: Adjustment in test sample in a random cross-validation split. certain dispersion. The behavior of Conv-1d is similar to these models, despite there being slightly more variability. The performance obtained with LSTM makes it the most suitable model for the modeling and prediction of electricity consumption. The forecast of the models in the test set is adjusted. Therefore, comparing the results with other similar analyses carried out recently, the performance shown in this study stands out. The accuracy shown (using nRMSE) in the less adjusted models, i.e., Conv-1D, SVR and MLP, is slightly higher, about 2%, than the models analyzed in [37; 39]. The rest of the models analyzed, i.e., RF, XGBoost and LSTM, show a better performance than any of the models implemented in the aforementioned research. Therefore, in addition to the good fit offered by RF and SVR for the forecast of electrical loads, the superior performance of LSTM is clear. The applied methodology offers a comprehensive comparison of different ML and DL models. The accuracy of the methods conducted is adequate, improving studies realized recently in the field of electricity consumption forecasting in buildings. As can be seen in the Results section, it is necessary to carry out a test method that considers the robustness of the models. In this way, the reliability of the results obtained can be analyzed. The use of the nRMSE and nMBE metrics to determine the dispersion and trend of the predictions appears adequate to effectively categorize the evaluated models.