## 1 Introduction
Among all of them, the categorisation of solar irradiance data is one of the earliest applied techniques [24], and in the literature there are many authors who have analysed and predicted the behaviour of irradiance profiles, using various procedures [24; 25; 26; 27]. In order to characterise daily irradiance profiles, several authors define a series of indices, such as the clearness index, which represents the proportion of measured irradiance and theoretical irradiance under clear sky conditions. There is also the variability or intermittency index, or the fractal dimension [28; 29; 30], which are parameters that take into account the deviation of the actual irradiance values from clear sky conditions. For their characterisation, the irradiance data recorded over time can be used directly or, alternatively, a transformation of these temporal data to the frequency domain can be performed beforehand [24]. All these types of data can be used to make classifications, in order to extract different types of days, and group those types of daily profiles with similar behaviour. Different authors use different methods to make these classifications, considering a greater or a lesser number of types of days [24; 25; 31; 32; 33; 34; 35; 36; 37]. Although obviously the production of a PV plant at each moment in time will depend on the amount of irradiance received by the solar panels and the temperature at which they operate, the meteorological conditions that occur throughout the day, such as the variation in ambient temperature and wind speed, as well as cloud cover and the speed at which clouds are moving, generally determine the operation conditions and the behaviour of the plant's components, so that the efficiency and yield values of both the modules and the inverters are modified throughout the day, and from one day to the next [38; 39]. This means that the ability of the components of the PV system to convert the received irradiance into electricity is not constant, but is influenced by the type of day that occurs. In this context, one of the main objectives of this work was to apply a classification algorithm to a nine-year history of records of the global irradiance received by the PV modules of a solar installation, in order to automatically find patterns or types of days. Once the days were classified according to the daily irradiance profiles, the dependence between the patterns of days found and the operation and performance of the PV plant components were also analysed, which is one of the main contributions of this paper. To obtain the different types of days, a clustering method was used, within the unsupervised automatic machine learning techniques, making use of the existing tools in the Python programming language. The method used in this work stands out for its simplicity and ease of application, and requires almost no changes to the irradiance data measured at the PV plant, so no data filtering or transformation are required [24]. If the weather forecast is known in advance, it will be possible to know beforehand the type of day that is going to take place, and from the results found, it will be possible to know what the plant's behavioural interval will be, which will obviously influence its production. Once the behaviour of the installation components were obtained on each type of day, another of the aims of this work was to show the reasons for the variation in this behaviour on different types of days. To this end, an analysis of the operation of its elements on clear sky days was carried out, comparing it with that obtained on days with a greater presence of fluctuations. On these non-stable days there may be peaks of irradiance higher than the values registered on clear sky days, due to the phenomenon known as cloud enhancement (CE) or irradiance enhancement (IE) [40], and the presence of these events was also analysed in this paper. According to [PERSON] et al. [41], although the CE phenomenon is well known, its effects on actual PV systems has not been thoroughly studied.