The Levenberg-Marquardt and Gradient Descent learning algorithms and hyperbolic tangent sigmoid, logarithmic sigmoid, and hard-limit transfer functions were used, with structures consisting of 1 to 5 hidden layers with 2 to 40 neurons in each hidden layer. The selected model was the Levenberg-Marquardt-Logarithmic Sigmoid (with a structure: 4/12/11/1), as it had the best generalising ability, a compact structure, a fast training process, and low memory consumption. ANN models were found to be effective in predicting energy consumption thanks to their computational speed, ability to handle complex nonlinear functions, and robustness even when full information is not available. Some authors went on to explore the impact of hybrid models on the overall performance of the forecasting process. For instance, the authors in [18] investigated the use of a hybrid approach by combining Singular Spectrum Analysis (SSA) and ANNs for day-ahead hourly load forecasting. The extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or an LSTM predictive layer. The model is further extended to include exogenous features, such as weather forecasts. The predictive performance is evaluated on two real-world datasets, and the results show that the decomposition step improves the relative performance of ANN models, with a combination of LSTM and SSA providing the best overall performance. Furthermore, [PERSON] et al. [19] proposed a model that predicts load demand for different hospital usage scenarios by using a dual hybrid combination of four simple models: Seasonal-ARIMA (SARIMA), SVR, MLP, and LSTM. Experimental results show that there are corresponding matching models for unique usage scenarios. For example, in one case, a single LSTM model performed better in predicting the demand for inpatient day shift surgery in a Norwegian hospital, while a hybrid model performed more effectively in most cases. [PERSON] et al. [20] proposed the use of multilayer perception BP neural networks as a forecasting algorithm to predict electricity consumption in Cellini medical clinic in Turin, using time series data of load data, day type, and weather data as input features, and Mean Absolute Percentage Error (MAPE), Daily Peak MAPE, Coefficient of the Variation of the Root Mean Square Error (CVRMSE), Mean Percentage Error (MPE), and MAPE under 5% as forecasting assessment metrics. In [21], the authors conducted a study to see which has a higher impact on the forecasting accuracy, the neural network or the learning algorithm. In their paper, the performance of MLP and Elman neural networks in electric load forecasting was compared. Using a dataset from a power utility in Thessaloniki, Greece, the authors performed short-term forecasting to predict one week ahead in order to test the network's generalisation ability. Different combinations of prediction order and the number of neurons in the hidden layer were used to find the architecture that would model the data most effectively. The MATLAB Neural Networks toolbox was used to initialise, train, and test both the MLP and Elman networks. The Mean Square Error (MSE) was used to measure network performance, and the results show that the efficiency of the learning algorithm is more important than the neural network model used for forecasting. The authors found that the Elman network models the considered electric load series better than the MLP network, but training the Elman network is two to five times slower than training the MLP network. The time required for training depends on the training data size and the number of network parameters. However, the learning algorithms used with the Elman network were not able to fully implement the richer structure of this network. A synthesis that includes the advantages and disadvantages of the techniques and algorithms proposed in the literature are presented in Tables 1 and 2.