# Load Forecasting with Machine Learning and Deep Learning Methods
## 2 Related Work
This same approach is carried out in [35], where the authors compare ARIMA, SARIMA, XGBoost, RFand LSTM by mean percentage error (MPE), MAPE, MAE and RMSE metrics. The authors of [36] investigate the use of SVR as a method for load prediction through a comparison with RF, ARIMA, XGBoost, and MLP, and the accuracy is assessed using MAPE. In [37], the prediction of electricity consumption each day of the week is evaluated by means of a CNN, comparing the results with RNN, ARIMA and MLP. MAPE, MAE and RMSE are used for error evaluation. The approach of [38] proposes to predict electricity demand by separating building based power from occupant-influenced demand. A three-hour redundancy period is added to account for flexible working hours. The method used is MLP and its performance is evaluated with RMSE. In [39], the methods for the prediction of electricity consumption in a building are evaluated. The authors use different metrics to assess performance, i.e., RMSE, MAE, magnitude of relative error (MRE), MAPE, and normalized mean bias error (nRMSE). In [40], a comparative analysis of the prediction of the energy demand of a building is carried out considering LR, SVR, MLP, extreme deep learning (EDL) and GBoost. To determine the most appropriate model, the performance is evaluated through cross-validation using the metrics R, MAE, RMSE, relative absolute error (RAE) and root relative squared error (RRSE). The framework adopted in [41] evaluates XGBoost, SVR and MLP for the prediction of the energy consumption of a building. Using a cross-validation model, the authors perform a performance assessment with CV(RMSE) and nMBE. Table 1 shows the main characteristics of each of the investigations discussed. This study proposes a robust methodology for the prediction of electricity consumption considering the trends of different ML and DL models. Furthermore, we carry out a test \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper ID** & **Models** & **Temporal Granularity** & **Error Metrics** & **Cross-Validation** & **Test Set** \\ \hline [31] & MLP, XGBoost, SVR & 30 min & MAE, MAPE, R & No & No \\ \hline [27] & LSTM, CNN-LSTM, XGBoost, RBFN & 30 min & RMSE, MAPE, MAE, R2 & No & Yes (16\%) \\ \hline [32] & SVR, LSTM, RF, GP & 1 h & MAE, sMAPE, RMSE & No & No \\ \hline [28] & MLP, LM-MLP & 1 day & MAE, RMSE & No & Yes (30\%) \\ \hline [33] & RF, RT, SVR & 1 h & PI, RMSE, MAPE, R2 & No & Yes (20\%) \\ \hline [34] & GBoost, RF, TOTW & 15 min & R2, CV(RMSE), nMBE & Yes, 5 folds & No \\ \hline [25] & ARIMA, LR, LSTM & 1 h & RMSE, MBE & No & Yes (25\%) \\ \hline [26] & MLP, SVR, RT, LR, SARIMA & 15 min & R, RMSE, MAE, MAPE, MaxAE & No & Yes (1 day) \\ \hline [35] & ARIMA, SARIMA, XGBoost, RF, LSTM & 1 h & MPE, MAPE, MAE, RMSE & No & Yes (40\%) \\ \hline [36] & SVR, RF, XGBoost, MLP & 30 min & MAPE & No & Yes (1 month) \\ \hline [37] & LSTM, CNN, ARIMA, MLP & 30 min & MAPE, MAE, RMSE & No & Yes (1 day) \\ \hline [38] & MLP & 1 h & RMSE & No & Yes (30\%) \\ \hline [39] & LR, GP, MLP, SVR & 1 h & RMSE, MAE, MRE, MAPE, nRMSE & No & Yes (20\%) \\ \hline [40] & LR, SVR, MLP, EDL, & & R, MAE, RMSE, RAE, RRSE & Yes, 5 folds & Yes (30\%) \\ \hline [41] & XGBoost, SVR, MLP & 1 h & CV(RMSE), nMBE & Yes, 3 folds & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the main characteristics of the current research in the field of energy forecasting in buildings. to show the performance of the results obtained, showing the models' effectiveness for prediction of moments in the test, including the validation, and future moments, i.e., the test set. This work aims to address the gaps in current studies, which are outlined below:  
* Despite the great opportunity presented by ML and DL algorithms, the characteristic variability of the different techniques has not been evaluated. Many studies focus on LSTM, MLP, RF or XGBoost but do not compare the quality of the results with those of classic ML and DL methods.