# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 1 Introduction  
During the last decade, worldwide installation of renewable generation plants has considerably increased. Among renewables, photovoltaic (_PV_) solar plants have been the most interesting in recent years, and it seems that they will be the most installed in the following years [1, 2]. During 2019, the last analyzed year in the Global Status Report [3], 201 GW of renewable power capacity were installed in the World; 115 GW of Solar _PV_ capacity, corresponding to more than 57% of total renewable additions. The solar PV cumulative installed capacity was raised to 633.7 GW by the end of 2019. The reason for the spectacular growth and prospect of this energy source lies in the constant development of the technology and its high reliability. This has made possible a drastic reduction in costs, which has favored both its large-scale implementation and small-scale Distributed Generation (_DG_). Many countries have already begun to review their climate and energy policies. Innovation in sustainable energy supply is, thus, crucial for providing reliable and clean energy sources and improving the quality of life on this planet. To achieve this goal, the idea of smart energy buildings or energy-neutral buildings has been launched. The main objective of an energy regulation of a building is to maintaininternal thermal comfort, as well as minimizing energy consumption [4]. \(PV\) systems and electric vehicles (\(EVs\)) integrated into local distribution systems are considered to be two of the keys to a sustainable future built environment in Smart Cities (\(SC\)) [5]. The term \"smart energy city\" has arisen in parallel with these developments, notably since at least the turn of the 2010s in connection with the energy-relevant components of smart cities [6]. In this scenario, ensuring energy production is a key factor in warranting plant profitability, and this has forced the design of increasingly intelligent and advanced operation and maintenance (\(O\&M\)) strategies. The \(O\&M\) of \(PV\) solar plants is critical for industry players. The development of new equipment and methodologies for its application in \(PV\) solar plants is necessary, and in this sense, research and industry in this area are rapidly evolving [7]. Some researchers have worked with real data from \(PV\) solar plants, and they have obtained important results showing defects in them [8]. The \(PV\) inverter is a critical element in \(PV\) solar plants, but so is the \(PV\) solar module. A \(PV\) solar module is made up of \(PV\) solar cells, and these cells can have different problems or defects, as shown in [9]. Although the knowledge at the \(PV\) solar cell level is interesting for operators and maintainers of \(PV\) solar plants, the measurements should be taken in the \(PV\) solar module. The detection of faults in \(PV\) solar modules is subject to obtaining field data, as currently, monitoring does not reach the module level in \(PV\) plants. Powerful and accurate data can be obtained by current-voltage (I-V) curves, both from the \(PV\) solar cell (when the cell contacts can be accessed) or at the \(PV\) solar module level [9; 10]. Nowadays, I-V tracers present some drawbacks, such as being only for the string level, working offline, or being expensive. However, advanced instruments recently developed allow automating the online module I-V curves acquisition without requiring the \(PV\) plant to be shut down [11]. Additionally, it is also possible to perform online dark I-V curves of modules in \(PV\) plants without the need to disconnect them from the string. For this, a combination of electronic boards in the \(PV\) modules and a bidirectional inverter is required, as presented in [12]. Another way to obtain field data is by taking images. Classically, thermographic imaging (_IRT_) has been used for early detection of hot spots [13], and in recent years this _IRT_ method has been carried out using drone flight [14; 15].