One hundred and two environmental and meteorological parameters have been investigated and compared with the performance of 20 soiling stations installed in the USA, in order to determine their ability to predict the soiling losses occurring on PV systems. The results of this investigation showed that the annual average of the daily mean particulate matter values recorded by monitoring stations deployed near the PV systems are the best soiling predictors, with coefficients of determination ( R 2 ) as high as 0.82. The precipitation pattern was also found to be relevant: among the different meteorological parameters, the average length of dry periods had the best correlation with the soiling ratio. A preliminary investigation of two‐variable regressions was attempted and resulted in an adjusted R 2 of 0.90 when a combination of PM 2.5 and a binary classification for the average length of the dry period was introduced. Copyright © 2017 John Wiley & Sons, Ltd. Coefficient of determination of the linear single‐variable correlations between the most significant soiling predictors considered in this study and the soiling ratios registered by 20 PV systems deployed in the USA.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An investigation of the key parameters for predicting PV soiling losses


    Contributors:

    Published in:

    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    53.36 Energiedirektumwandler, elektrische Energiespeicher



    Losses produced by soiling in the incoming radiation to photovoltaic modules

    Zorrilla‐Casanova, José | Online Contents | 2013



    Vehicle Soiling Simulations

    Weston, S. / Mendonca, F. | British Library Conference Proceedings | 2006


    Methodical Investigation of Vehicle Side Glass Soiling Phenomena

    Landwehr, Thomas / Kuthada, Timo / Wiedemann, Jochen | Springer Verlag | 2017