Weather Foundation Model Enhanced Decentralized Photovoltaic Power Forecasting Through Spatio-temporal Knowledge Distillation

He Fang, Jiaqi FAN, Yang Deng, Xiaoyang Zhang, Dan Wang

Abstract:

The solar photovoltaic power forecasting (SPPF)
of a PV system is vital for 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.