Machine learning-based prediction of soiling losses in photovoltaic modules under different cleaning frequencies: an experimental investigation.

Sci Rep · 2026 · PMC13237206 · PMID 41981012

Europe PMC DOI

hedged sentences

0.0010

closest p · 0.0× alpha

0.0480

boldest claim

The sentences

highly significantp < 0.001actually significant

Cleaning frequency also shows a highly significant negative effect ( p < 0.001), indicating the reduction in current with increasing days between cleaning.

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near-significantp = 0.048actually significant

Wind speed shows a near-significant positive influence ( p = 0.048), reflecting its dual role in either removing or redistributing dust.

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