IEEE PES International Meeting · 2026

PEARL: A Physics-Enhanced Adaptive Residual Learning Framework for PV Modeling

Yikai Lu, Yujie Chen, Tongxin Li

IEEE PES International Meeting

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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.

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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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