## 1 Introduction  
Nowadays, energy demand is constantly increasing. Finite resources such as fossil fuels are being depleted, and global pollution is reaching alarming levels. These facts lead to the current necessity to urgently implement renewable resources. In this regard, wind energy plays a crucial role in the decarbonization process of society [1; 2; 3; 4] and constitutes the most relevant energy contributor to the renewable sector [5]. Depending on the orientation of the rotation axis, wind turbines can be categorized into HAWTs (horizontal axis wind turbines) and VAWTs (vertical axis wind turbines). HAWTs are the most employed ones, mainly installed in high-power wind farms. An important disadvantage is that HAWTs are too dependent on wind conditions [6; 7; 8]. On the other hand, VAWTs are less common but also interesting due to their capacity to capture wind from all directions and their acceptable performance under poor wind conditions. These characteristics make VAWTs appropriate for small power generation applications, especially in urban environments [9]. VAWTs are basically categorized into Darrieus (based on lift) and Savonius (based on drag). The Savonius VAWTs are recommended when the wind velocity is low due to their lower starting torque, i.e., they need lower wind velocities to start [10]. Nevertheless, the efficiency of the Savonius turbine is lower than the Darrieus turbine. In order to increment the efficiency of the Savonius turbine, several parameters have been analyzed in the literature. Several augmentation techniques have been proposed, such as nozzles,deflectors, guide vanes, end plates, etc. Another improvement of the Savonius turbine relies on the blade profile. Some authors proposed modifications to the semicircular blade profile [11; 12; 13; 14; 15; 16]. The efficiency can also be increased through some parameters related to the rotor design, such as the aspect ratio (AR), overlap (O), separation gap (SG), twist angle (TA), number of blades, etc. [17; 18; 19]. Figure 1a illustrates a 3D view of a Savonius turbine. It is composed of semicircular blades vertically positioned along the rotation axis. The O is the distance that an inside edge penetrates into another. The SG is the width separating the inside edges of the blades. The TA is the angle between the upper and lower sections of the blades. The turbine shown in Figure 1a has a zero value of AR, O, SG, and TA. The AR, O, and SG are illustrated in Figure 1b and the TA in Figure 1c. The AR is the relation between the height and the diameter, AR = H/D. Unfortunately, the relations between these parameters and the efficiency are too complex, and there is no analytical relation between them. The determination of the most appropriate combination of AR, SG, O, etc., is a complex nonlinear problem and there is a huge number of combinations. Because of this, a tool is required to efficiently predict the power output. In this regard, artificial neural networks (ANNs) may constitute a useful solution for these types of problems. In fact, ANNs are recommended for problems that are too complex to solve and where enough data can be obtained. The principle of operation of ANNs consists of simulating the biological neuron structure and learning capacity. ANNs employ artificial neurons similarly to biological neurons in the human brain. Through the training process, an ANN takes the input/s variable/s and inter-relates them to obtain the output/s variable/s. An important advantage is that a well-trained ANN is too fast to predict the output variable/s. ANNs are able to learn the relationships between the inputs and outputs and provide output predictions with a higher accurate prediction than traditional methods [20; 21]. The nonlinear application of ANNs enables them to address many problems that are very difficult to be modeled mathematically. ANNs avoid complex physical and/or mathematical models. In recent years, ANNs have been widely used in many fields, such as engineering, science, economy, etc.