###### Abstract
Among the methods presented, the impact of black box modelling stands out in recent years, mainly because of the development of machine learning techniques, specifically, the deep learning techniques [27]. These replicate how the human brain works, being the key point in offering models the ability to learn. In this way, with deep learning techniques, computers have the ability to identify complex patterns in the data without being explicitly programmed, offering accurate predictions at a low cost [28]. Deep Learning techniques have enormous potential, however, mainly because the dependence on a variety of hyperparameters, the performance of the algorithm can vary significantly, generating erroneous results under certain circumstances, either overfitting or underfitting. These hyperparameters correspond to the number of layers, the number of nodes per layer, the type of activation function in each layer, the selection of a suitable learning rate, considering if it is necessary to consider a decay rate in the problem, the optimizer, or the number of epochs and batches in the model [29]. Nowadays, the selection of the best values or settings and how to adjust each hyperparameter to achieve the best performance is still unclear and is an open question in computer sciences [30]. In this way, the search for the appropriate configuration of these hyperparameters is a challenging problem and the applicability of these techniques on modeling and prediction of electrical energy is in continuous improvement [31]. Deep learning methods are usually divided into three branches: standard neural networks (SNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). The fields of application are diverse, SNNs being habitually linked to areas as building energy [32] or environment (e.g., saline particles classification [33] and botanical origin of honeys prediction [34]); CNN, to image recognition (e.g., object detection [35] and face recognition [36]), or autonomous driving (e.g., road architecture recognition [37] and sign detection [38]); and RNN, to speech recognition [39] or translation [40]. The common characteristic is the structure, from some input data, it is sought to extract a pattern in order to extrapolate the results to new data. However, depending on the type of data used, the results will have greater accuracy. So SNN are often used with tabular data, CNN, with image data and RNN, with sequence data [27]. Similar tasks to those proposed here have been solved with deep learning or related techniques: prediction of the energy consumption of a green building [41], implementation of demand-response programs at a multiple-building scale [42], forecasting of the behavior of a group of residential buildings [43], scheduling of the storage system based on reducing the peak load [44] or forecasting in smart home applications [45]. To carry out this study, the SNN are applied to predict the electricity consumption of an office building. Thus, the fitting of the SNN to problems in the field of study is widely demonstrated; it is possible to obtain a very good accuracy despite the simplicity they present in their structure [46]. In addition, the adjustment of this type of neural network also has great accuracy in the field of photovoltaic production prediction. It is able easy to extend the models [47]. In [48] a RF-XGBoost model was used for the prediction of electrical energy from temperature, humidity, air density and pressure. In [49] different machine learning models (SNN, support vector machine, and RNN) were compared for the prediction of photovoltaic production, heating, cooling, and illumination from outdoor air-dry bulb temperature, outdoor humidity, global solar radiation, cloudiness ratio, schedules of occupants and office equipment, indoor air-dry bulb temperature, and photovoltaic surface temperature.