IEEE PES International Meeting · 2026
PEARL: A Physics-Enhanced Adaptive Residual Learning Framework for PV Modeling
IEEE PES International Meeting
Can learned residuals improve physical models of solar generation?
PEARL separates a physical photovoltaic simulator from a learned residual correction. A controlled benchmark compares several machine learning architectures and a coupled modeling baseline, examining accuracy while retaining the physical model as an interpretable reference.
Research themes
Cite this paper
Yikai Lu, Yujie Chen, Tongxin Li. PEARL: A Physics-Enhanced Adaptive Residual Learning Framework for PV Modeling. IEEE PES International Meeting, 2026. https://doi.org/10.1109/PESIM67009.2026.11438881
BibTeX
@inproceedings{tongxin-pearl,
title = {{PEARL: A Physics-Enhanced Adaptive Residual Learning Framework for PV Modeling}},
author = {Yikai Lu and Yujie Chen and Tongxin Li},
year = {2026},
booktitle = {IEEE PES International Meeting},
url = {https://doi.org/10.1109/PESIM67009.2026.11438881},
doi = {10.1109/PESIM67009.2026.11438881}
}
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