Abstract
In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.
Highlights
Renewable energy sources, which are clean and inexhaustible resources that are used for electricity generation through different technologies, are currently playing a crucial role in the race to achieve some of the Sustainable Development Goals of the United Nations [1], such as Goal 7, which aims to ensure access to affordable, reliable, sustainable and modern energy for all
There are two periods, in 2012 and 2016, where, and Discussion failures3.inResults the monitoring system, no data records were available, and which, as ind Figure 1 shows the total irradiance values received on the plane of the PV modules, above, which were were excluded from the classification process
Spain foraand anine-year nine-year monitoring periodthe were categorised by applying the k-means clustering method, were monitoring period were categorised by applying the k-means clustering method, and monitoring period were categorised by applying the k-means clustering method, andwere were grouped into nine different day types
Summary
Renewable energy sources, which are clean and inexhaustible resources that are used for electricity generation through different technologies, are currently playing a crucial role in the race to achieve some of the Sustainable Development Goals of the United Nations [1], such as Goal 7, which aims to ensure access to affordable, reliable, sustainable and modern energy for all. Is to produce 57% of the world’s energy from renewable sources by 2030, up from 26%. Today, more than doubling the existing share, so this decade is considered to be the one in which renewable energies will drive the global energy transformation [2]. Even more ambitious are the objectives proposed by European countries through the European Green. Deal, whose objective, in the fight against the threat of climate change and environmental degradation, is that there will be no net emissions of greenhouse gases by 2050 [3]. In 2019, this was the most installed energy source among both renewables and non-renewable energies, accounting for 40% of new global capacity [4,5,6].
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