Bayesian Regression-Based Power Loss Probabilistic Estimation of Dust-Accumulated PV Systems

This paper proposes a Bayesian regression neural network (SolarBRNN) combined with a feature pyramid network (FPN) for photovoltaic power loss (PVPL) prediction. The model effectively captures multi-scale features from images by incorporating the feature pyramid module, enhancing its ability to dete...

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Bibliographic Details
Published inChinese Control and Decision Conference pp. 3495 - 3501
Main Authors Li, Chunrong, Chen, Xinyi, Shen, Yu, Zhang, Kanjian, Fang, Shixiong, Wei, Haikun
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.05.2025
Subjects
Accuracy
Adaptation models
Bayes methods
Bayesian regression neural network
Computational modeling
dust
Estimation
Feature extraction
feature pyramid network
Neural networks
photovoltaic power loss
power loss estimation
Predictive models
Systems operation
Uncertainty
uncertainty quantification
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ISSN1948-9447
DOI10.1109/CCDC65474.2025.11090541

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Summary:This paper proposes a Bayesian regression neural network (SolarBRNN) combined with a feature pyramid network (FPN) for photovoltaic power loss (PVPL) prediction. The model effectively captures multi-scale features from images by incorporating the feature pyramid module, enhancing its ability to detect the distribution of dust and its impact on power loss. Through Bayesian regression, the model can not only predict the mean power loss but also quantify the uncertainty of the predictions, providing more reliable information for system operation. Experimental results demonstrate that, compared to quantile regression methods, the SolarBRNN model significantly improves performance in terms of prediction standard deviation and the confidence interval coverage probability (CICP). Notably, the CICP indicator increases by 12%, indicating better prediction accuracy and stronger adaptability.
ISSN:1948-9447
DOI:10.1109/CCDC65474.2025.11090541