TimesFM-Based Photovoltaic Power Forecasting with Few-Shot Residual Adaptation Under Data Scarcity

Accurate photovoltaic (PV) power forecasting is becoming increasingly important for the secure and economical operation of renewable-rich power systems. However, conventional data-driven forecasting methods generally rely on sufficient site-specific historical observations, limiting their applicability to newly commissioned PV stations with severe data scarcity; meanwhile, forecasting performance is still predominantly evaluated using statistical error metrics, which cannot fully reflect its practical value in power system operation. To address these issues, this study develops a PV forecasting framework based on the pretrained time-series foundation model TimesFM, which exploits transferable temporal representations to enable zero-shot forecasting with limited target-site data. A lightweight residual quantile few-shot adaptation scheme is further introduced to learn station-specific forecasting deviations from limited historical observations while keeping the pretrained forecasting backbone fixed, thereby improving target-site adaptation and forecasting capability. In addition, a multi-dimensional power-system-operation-oriented evaluation framework is established to assess forecasting performance from economic, flexibility, regulation, and network-operation perspectives, together with different PV integration locations and installed capacities. Experimental results demonstrate that for the investigated CYT Station 1 case, the proposed method improves deterministic and probabilistic forecasting performance relative to zero-shot TimesFM and achieves substantial adaptation gains using limited target-site observations. The improved forecasts also reduce forecasting-induced operating costs and reserve requirements and yield system responses closer to those obtained with actual PV generation, confirming that forecasting improvements can be translated into measurable operational benefits.