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

Abstract

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).

Modeling soiling losses for rooftop PV systems in suburban areas with nearby forest in Madrid

“…Since IeV curves are being continuously monitored in the experimental facility, the performance index computed for soiling estimations is calculated from the temperature-corrected short-circuit current. The short-circuit current of a PV module is proportional to the irradiance so that it seems to be the best parameter to characterize soiling losses, since soiling implies a reduction in the effective irradiance [32,33]. Therefore, the performance index computed from the short-circuit current is defined, in analogy with the performance ratio, as, PI Isc ¼ 1000 I sc ð1 a SC ðT mod 25ÞÞ I STC sc G POA (3) where I sc and I STC sc are the short-circuit currents of the module at environmental conditions and at STC (Standard Test Conditions, 1000 W m 2 and 25 C), respectively; a SC is the temperature coefficient of the short-circuit current; T mod is the module temperature; and G POA is the plane of the array irradiance.…”

Advancing Grid-Connected Renewable Generation Systems

“…The authors of [3] raised the issue of soiling in PV installations. The soiling will provoke a reduction in the performance of the PV plant, due to energy losses and degradation of the panels.…”

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

“…The ANN model showed better performance compared to the MLR model. Perez et al [13] used ANN to estimate losses resulting from dust. With new technology, ANN is successful in predicting losses and its performance metrics are normalized root mean square errors (NRMSE) = 6.79 and R = 0.91.…”