arXiv Machine Learning September 24, 2026 By Rakib Abdullah, K. M. Tahlil Mahfuz Faruk

Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity

Read the original on arXiv Machine Learning →

The paper introduces a transfer learning framework paired with Conformalized Quantile Regression (CQR) to enhance solar PV power forecasting in regions with limited historical data due to load shedding. Using a source-domain dataset from Alice Springs, Australia, the model is pretrained and then adapted to simulated Bangladesh PV data with varying data availability. Experiments show that transfer learning can reduce RMSE by up to 23.7% with only one month of target data, and the combined approach achieves 94.3% empirical coverage with prediction intervals 14% narrower than without transfer learning.

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