The solar photovoltaic power forecasting (SPPF) of a PV system is vital for the downstream power estimation. While approaches for recent decentralized PV systems require customized models for each PV installation, this method is labor-intensive and not scalable. Therefore, developing a general SPPF model for a decentralized PV system is essential. The primary challenge in developing such a model is accounting for regional weather variations. Recent advancements in weather foundation models (WFMs) offer a promising opportunity, providing accurate forecasts with reduced computational demands. However, integrating WFMs into SPPF models remains challenging due to the complexity of WFMs. This paper introduces a novel approach, spatio-temporal knowledge distillation (STKD), to efficiently adapt WFMs for SPPF. The proposed STKD-PV models leverage regional weather and PV power data to forecast power generation from six hours to a day ahead. Globally evaluated across six datasets, STKD-PV models demonstrate superior performance compared to state-of-the-art (SOTA) time-series models and fine-tuned WFMs, achieving significant improvements in forecasting accuracy. This study marks the first application of knowledge distillation from WFMs to SPPF, offering a scalable and cost-effective solution for decentralized PV systems.