@inproceedings{4e180a6936aa4f90b77683c191ec7d0b,
title = "Soiling Rate Determination from Referenced Systems in Desert Climate using PVInsight Soiling Algorithm",
abstract = "We present a comparison of two methods to estimate the soiling losses from photovoltaic (PV) system operational data. We evaluate 21 months of data from an outdoor soiling test in a hot, desert climate with a reference system which was cleaned once a week. First, we present an analyst-based result that uses accepted industry practices to estimate trends based on data fit and records of cleaning and storm events, representative of an industry-standard, labor-intensive approach. Second, we present a completely automated analysis, in which the cleaning events are selected through an unsupervised statistical learning algorithm. The algorithm generates estimates of linear soiling rates that are in close agreement with the rates determined by the human analyst, at orders of magnitude faster speed. The speed of processing is particularly advantageous in analysis of multiple setups and long duration data (years of minute(s) data collection).",
keywords = "PVInsight, data analysis, hot desert climate soiling, machine learning, soiling, solar energy, statistical learning",
author = "Elsa Kam-Lum and Meyers, {Bennet E.} and Damien Cosme and Brahim Aissa and Giovanni Scabbia",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 48th IEEE Photovoltaic Specialists Conference, PVSC 2021 ; Conference date: 20-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
day = "20",
doi = "10.1109/PVSC43889.2021.9518459",
language = "English",
series = "Conference Record of the IEEE Photovoltaic Specialists Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2552--2554",
booktitle = "2021 IEEE 48th Photovoltaic Specialists Conference, PVSC 2021",
address = "United States",
}