## 4 Conclusions  
In this work, the daily irradiance profiles recorded in southern Spain for a nine-year monitoring period were categorised by applying the _k-means_ clustering method, and were grouped into nine different day types. According to the total daily irradiance values, three categories were obtained, named low, medium and high, and the fluctuation of this parameter throughout the day, due to the presence of clouds, gave rise to three categories within each of the above. It is considered that the methodology was efficient and easy to apply, and it allowed the irradiance profiles recorded for the monitoring period analysed to be correctly classified. By determining the operation of the plant elements during this period, it was possible to observe that the behaviour of the modules and the inverter depended on the type of day, such that the weather forecasts would allow us to know the range of efficiency with which the plant elements are going to work on the following day. For large PV plants, with high nominal power values, a variation of several units in the efficiency of the elements could represent a significant amount of energy. The opposite behaviour of modules and inverter against days with fluctuations is what makes the _PR_ values so similar in some of the clusters, regardless of the increase in fluctuations. It was found that inverters worked with a better average efficiency on days with higher irradiance values and lower fluctuations, i.e., days with no or little cloud cover, whereas PV modules improved their average efficiency on days with fluctuations and cloud passages. Figure 23: Daily profiles of inverter losses obtained during 12, 14 and 17 May 2011. Figure 24: Daily profiles of inverter efficiency obtained during 12, 14 and 17 May 2011. This may be due to the presence, on those days, of higher irradiance values than on clear sky days, due to the so-called CE effect, and which, together with lower ambient and module temperatures due to the shading effect of the clouds, mean that at those instants of time there are not only peaks in production, but also that the module works with lower temperature losses and therefore with better yields. It was observed that it is important to take into account the presence of these events when sizing the inverter, in order to improve its operation and lifetime. The tendency of the results obtained in this work could be applied to PV installations with similar configurations to the one analysed in this work, and with analogous meteorological conditions, such as Mediterranean countries. In any case, the methodology proposed in this work can also be easily applied to data monitored in PV installations located in geographical locations with very different meteorological conditions, in order to analyse the behaviour of their elements on different types of days. In future works, classification could be carried out using other algorithms, and performing classifications with different numbers of clusters. It would also be interesting to modify the size of the data set used to perform the classification in order to optimise the minimum amount necessary to obtain adequate results, as well as to apply the study to the operation of other PV installations with different configurations, in order to be able to generalise the results found. Another important aspect to analyse, which was also beyond the scope of this study, would be to determine the probabilities of obtaining a type of day depending on the type that is present on the preceding day or on the previous days. With respect to the behaviour of the components of the installation in the presence of the CE events, the analysis was carried out in a small PV installation, but it would be very interesting to extend this study to a larger utility-scale plant, which requires a larger surface area for the PV panels, in order to study the limitation in size of the affected land areas by the CE effect, and to study how the cloud speed affects it.