Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant's performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE).