## 1 Introduction
Numerous authors have conducted various studies related to the analysis of the performance of components of grid-connected PV plants in different geographical locations, with different climatic conditions, studying the different factors that can influence it [49]. To name a few, [PERSON] et al. analysed the performance of a plant in Greece for six years, studying the effects of degradation [50], while [PERSON] et al. analysed the performance of a rooftop plant in Turkey [51]. [PERSON] et al. carried out a performance analysis at a utility-scale located in a rainy tropical monsoon climate [52], [PERSON] et al. in a hot and dry climate [53], [PERSON] et al. in a dry arid climate in Algeria [54] and [PERSON] et al. in a maritime desert climate [55]. [PERSON] et al. analysed the performance of PV installations located in water channels in India [56]. [PERSON] et al. analysed the performance of plants located in various parts of the world [57] and found that their performance has improved over the years due to improved technologies. [PERSON] et al. also compared the performance of panels of different technologies, such as monocrystalline, polycrystalline and amorphous Si, analysing their dependence on weather conditions [58]. The performance of technologies was also compared in the work of [PERSON] et al., in this case at a plant in Kuwait [59]. This type of performance analysis is usually based on the IEC 61724 PV system performance--Part 1: Monitoring standard, which regulates the parameters that must be measured and calculated to carry out this performance analysis depending on the type of PV installation [60]. With regard to the integration of this type of renewable generation system into the electricity grid, it is also important to have production forecasting systems that make it possible to anticipate the amount of energy that will be produced, and the fluctuations and ramps in this production, which will help in the sizing of storage systems to support the management and participation in the electricity market of the production of this type of energy sources [30; 48; 61; 62; 63]. Artificial intelligence techniques such as machine learning or deep learning are making it possible to address this type of question, and numerous tools are already available [64; 65; 66; 67]. On the other hand, the aim is to minimise the impact of this production on the deviation of the parameters of the electricity network signal, so that these are within the supply quality intervals imposed by the regulations (standard EN-50160 Voltage characteristics of electricity supplied by public electricity networks [68]) for the electricity signal that reaches consumers and, thus, prevent the operation of these plants from exceeding the limits that could cause damage to the elements of the network [69; 70; 71; 72]. For all these issues, it is essential to first have monitoring systems in these plants that make it possible to know their operating parameters, as well as to obtain information on what is happening at their point of common coupling (PCC) and to have access to all the parameters that are recorded in them. Numerous studies have addressed the development of monitoring systems that make this information available [73; 74; 75; 31; 76; 77]. However, the cost and complexity of existing PV monitoring systems restrict their use to large-scale PV plants [78], and, most of the time, private PV systems are not being checked or supervised [75]. A PV monitoring system should be low-cost to be broadly accepted [75]. Although the increasing number of smaller-scale plants currently in operation do not usually have such monitoring systems in place, many of them do have the data recorded by the plants' own inverters, which can be used to analyse the plant's performance without the need for additional measuring equipment with an extra cost [69; 79; 80].