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The aim is to enhance short-term photovoltaic power prediction by integrating aerosol optical features.

March 4, 2026SustainabilityOpen Access

Improving Photovoltaic Power Forecasting Accuracy by Integrating Aerosol Optical Features: A Dual-Channel Deep Learning Approach

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This research integrates aerosol optical features with deep learning to enhance power forecasting accuracy in varying atmospheric conditions.

The aim is to enhance short-term photovoltaic power prediction by integrating aerosol optical features.
Developed a dual-channel encoder–decoder network using BiLSTM and iTransformer.
Constructed a high-dimensional aerosol optical feature set from OPAC database.
Optimized features with minimum redundancy maximum relevance (mRMR) algorithm.
Categorized prediction scenarios into polluted and clean regimes through K-means clustering.
Validated method with data from a PV station and Copernicus Atmosphere Monitoring Service.
Achieved approximately 29.83% reduction in mean absolute error (MAE) on polluted days.
Achieved approximately 15.22% reduction in MAE on clean days.
Highlighted distinct physical mechanisms driving predictions related to extinction and scattering.

Yang et al. (2026) studied this question.

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This research integrates aerosol optical features with deep learning to enhance power forecasting accuracy in varying atmospheric conditions.