发表机构
Green University of Bangladesh（孟加拉国绿色大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对负荷削减导致数据稀缺的光伏预测难题，提出结合共形分位数回归的迁移学习框架，显著提升点预测精度与区间可靠性。
AI 中文摘要
在受负荷削减影响的地区，太阳能光伏（PV）预测具有挑战性，因为可靠的历史观测数据稀缺。本研究提出了一种结合共形分位数回归（CQR）的迁移学习框架，以在严重数据稀缺条件下提高光伏功率预测并提供可靠的区间估计。使用来自澳大利亚爱丽丝泉的源域光伏数据集预训练一个时间序列预测模型，然后将其适配到模拟的孟加拉国光伏数据，这些数据代表不同的历史数据可用性水平。实验结果表明，当仅有一个月的目标域数据可用时，迁移学习可将均方根误差（RMSE）降低高达23.7%，而在有三个月数据时降低13.7%。所提出的迁移学习加CQR框架在拥有三个月目标数据时实现了94.3%的经验覆盖率，同时产生的预测区间比不使用迁移学习获得的区间窄14%。这些结果表明，将迁移学习与共形不确定性量化相结合，可以在目标域光伏数据严重受限时同时提高点预测准确性和不确定性可靠性。
英文摘要
Solar photovoltaic (PV) forecasting in regions affected by load shedding is challenging because reliable historical observations are scarce. This study proposes a transfer learning framework combined with Conformalized Quantile Regression (CQR) to improve PV power forecasting and provide reliable uncertainty estimates under severe data scarcity. A source-domain PV dataset from Alice Springs, Australia, is used to pretrain a temporal forecasting model, which is then adapted to simulated Bangladesh PV data representing different levels of historical availability. Experimental results show that transfer learning reduces RMSE by up to 23.7% when only one month of target-domain data is available and by 13.7% with three months of data. The proposed Transfer Learning plus CQR framework achieves 94.3% empirical coverage with three months of target data while producing prediction intervals that are 14% narrower than those obtained without transfer learning. These results demonstrate that combining transfer learning with conformal uncertainty quantification can improve both point forecasting accuracy and uncertainty reliability when target-domain PV data are severely limited.
Comments6 pages, 4 figures, 1st International Conference on Next-Generation Electrical & Electronics, Computer Systems, and Technologies (iCONEECT 2026)
