# Load Forecasting with Machine Learning and Deep Learning Methods
## 2 Related Work  
Electricity consumption forecasting plays a vital role in efficient energy management and planning in buildings. Research in this field focuses on investigating topics such as smart grids, renewable energy integration or local energy communities [23; 24]. ML and DL models have revolutionized this research by providing accurate and reliable predictions. Time series models like the autoregressive integrated moving average model (ARIMA) or the seasonal autoregressive integrated moving average (SARIMA) have long been used for energy forecasting in this sector, capturing seasonality and trends in historical consumption data [25; 26]. However, with advancements in ML and DL models like those presented in this study, powerful tools for capturing complex patterns and dependencies in electricity consumption have emerged. These models can handle non-linear relationships and long-term dependencies, making them suitable for energy forecasting in buildings [22]. Hybrid models that combine multiple forecasting techniques have also shown promise, leveraging the strengths of different algorithms to enhance prediction accuracy [27; 28]. The effectiveness of ML and DL models relies on feature engineering techniques that extract relevant information from the data, enabling the models to capture the key factors influencing electricity consumption. The application of these techniques enables better energy management decisions, facilitates grid stability, and supports the integration of distributed generation [29; 30]. The continuous advancements in this field provide precise and timely forecasts for effective energy planning and optimization. In [31], a method is presented that generates parallel energy consumption data using generative adversarial networks (GAN) and combines the generated data with the original data to improve the performance of energy consumption prediction in buildings. The authors use MLP, XGBoost and SVR, and evaluate the results using mean absolute error (MAE), mean absolute percentage error (MAPE) and Pearson correlation coefficient\({}^{\text{\textregistered}}\). In [27], a method is proposed that jointly integrates the CNN and LSTM for the prediction of electric charges. The authors compare performance with other models, i.e., LSTM, XGBoost, and radial basis functional network (RBFN), by using root mean square error (RMSE), MAPE metrics. MAE and goodness of fit (R\({}^{2}\)). In [32], a non-parametric regression model is proposed to predict electricity consumption in buildings. The model uses the Gaussian process (GP) for the selection of the input data. In the study, SVR, LSTM and RF are evaluated to compare the effectiveness of the proposed model. The error evaluation is carried out with symmetric mean absolute percentage error (sMAPE), MAE and RMSE. The study carried out in [28] develops strategies for predictive control using neural networks to predict energy consumption in buildings. The MLP and its optimization with the Levenberg-Marquardt algorithm are evaluated. The performance is evaluated with MAE and RMSE. In [33], an analysis is carried out that evaluates the performance of RF, regression tree (RT) and SVR for the prediction of energy consumption in buildings. The evaluation indices are MAPE, R2, RMSE and PI. In [34], the authors compare the gradient boosting (GB), RF, and time of week and temperature model (TOWT) methods for predicting energy consumption in buildings by applying cross-validation. In this analysis, the performance of the models is evaluated using R2, coefficient of variation of the RMSE (CV(RMSE)) and normalized mean bias error (nMBE). In [25], various techniques for predicting building loads are compared. LR, LSTM and ARIMA are used. The evaluation of the error is performed using RMSE and mean bias error (MBE). This same comparative analysis is carried out in [26], where the authors compare MLP, SVR, RT, linear regression (LR) and SARIMA. The performance of the models is determined with R, RMSE, MAE, MAPE and maximum absolute error (MaxAE).