Data-driven soiling detection in PV modules
- URL: http://arxiv.org/abs/2301.12939v1
- Date: Mon, 30 Jan 2023 14:35:47 GMT
- Title: Data-driven soiling detection in PV modules
- Authors: Alexandros Kalimeris, Ioannis Psarros, Giorgos Giannopoulos, Manolis
Terrovitis, George Papastefanatos, Gregory Kotsis
- Abstract summary: We study the problem of estimating the soiling ratio in photo-voltaic (PV) modules.
A key advantage of our algorithms is that they estimate soiling, without needing to train on labelled data.
Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.
- Score: 58.6906336996604
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Soiling is the accumulation of dirt in solar panels which leads to a
decreasing trend in solar energy yield and may be the cause of vast revenue
losses. The effect of soiling can be reduced by washing the panels, which is,
however, a procedure of non-negligible cost. Moreover, soiling monitoring
systems are often unreliable or very costly. We study the problem of estimating
the soiling ratio in photo-voltaic (PV) modules, i.e., the ratio of the real
power output to the power output that would be produced if solar panels were
clean. A key advantage of our algorithms is that they estimate soiling, without
needing to train on labelled data, i.e., periods of explicitly monitoring the
soiling in each park, and without relying on generic analytical formulas which
do not take into account the peculiarities of each installation. We consider as
input a time series comprising a minimum set of measurements, that are
available to most PV park operators. Our experimental evaluation shows that we
significantly outperform current state-of-the-art methods for estimating
soiling ratio.
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