A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants.

This paper presents a comparison between two different techniques for the determination of the effect of soiling on large scale photovoltaic plants. Four Bayesian Neural Network (BNN) models have been developed in order to calculate the performance at Standard Test Conditions (STC) of two plants installed in Southern Italy before and after a complete clean-up of their modules. The differences between the STC power before and after the clean-up represent the losses due to the soiling effect. The results obtained with the BNN models are compared with the ones calculated with a well known regression model. Although the soiling effect can have a significant impact on the PV system performance and specific models developed are applicable only to the specific location in which the testing was conducted, this study is of great importance because it suggests a procedure to be used in order to give the necessary confidence to operation and maintenance personnel in applying the right schedule of clean-ups by making the right compromise between washing cost and losses in energy production.