# Load Forecasting with Machine Learning and Deep Learning Methods
## 7 Conclusions  
The impact of this research is related to the need to characterize electrical loads and act on them to improve building performance. This study can serve as the basis for an improvement in the analysis of smart buildings or smart grids to provide benefits such as optimizing distributed production, storage systems, and the presence of electric vehicles. The conclusions are summarized in the following points:  
* The methodology used shows in detail the robustness and tendency of the studied models based on a comparative analysis in an effective and reliable manner.
* There is a trend suggesting that DL is better than ML, mainly due to the inclusion of missing values to conserve the data continuity.
* The performance of LSTM stands out with excellent results for predicting electrical loads, mainly because of the importance of the efficient modeling of the past input features, which in this study, were 6 h intervals.
* There is no improvement in using XGBoost over RF since these techniques seem to model the electric power demand in a similar way.
* The behavior of XGBoost, RF and Conv-1D is similar. Although they seem to follow the electric energy trends, they cannot be considered good predictors as they underestimate residual demands.
* The SNN, SVR have worse performance than LSTM.  
The limitations of the research carried out are based on the need to generalize the study to different contexts. Different power profiles may require additional validation and adaptation. Also, the techniques employed have been developed on a limited subset of hyperparameters. The effectiveness of the models can vary according to the values used. Future work in the application of the advances obtained in this study could involve the integration of the techniques in a management system, the use of advanced architectures or the employment of real-time forecasting.  
**Author Contributions:** Conceptualization, D.V. and M.C.-C.; methodology, M.C.-C.; software, M.C.-C. and M.M.-C.; validation, M.C.-C. and D.V.; formal analysis, M.C.-C. and S.R.; investigation, M.C.-C.; resources, D.V., P.E.-O. and S.R.; data curation, M.C.-C.; writing--original draft preparation, M.C.-C.; writing--review and editing, M.M.-C.; visualization, D.V.; supervision, P.E.-O. and S.R.; project administration, P.E.-O. and S.R.; funding acquisition, P.E.-O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Ministry of Science, Innovation and Universities of the Spanish Government (PV Smart project TED2021-130677B-I00 and the grant FPU19/01187), the Universidade de Vigo, Spain (grant 00 VI 131H 6410211), and the European Group for territorial cooperation Galicia-North of Portugal (GNP, AECT) through the IACOBUS program of research stays.  
**Data Availability Statement:** Data utilized in this manuscript can be found in References [45,46].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
Preprocessing  
\(ub\)  
\(lb\)  
\(lQR\)  
\(q\)  
\(Q3\)  
\(Q1\)  
\(q\)  
\(R\)  
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(r\)  
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