{"type":"ScholarlyArticle","title":"Weather Foundation Model Enhanced Decentralized Photovoltaic Power Forecasting Through Spatio-temporal Knowledge Distillation","authors":[{"name":"Fang He","slug":"fang-he","given":"Fang","family":"He","affiliation":""},{"name":"Jiaqi Fan","slug":"jiaqi-fan","given":"Jiaqi","family":"Fan","affiliation":""},{"name":"Yang Deng","slug":"yang-deng","given":"Yang","family":"Deng","affiliation":""},{"name":"Xiaoyang Zhang","slug":"xiaoyang-zhang","given":"Xiaoyang","family":"Zhang","affiliation":""},{"name":"Ka Tai Lau","slug":"ka-tai-lau","given":"Ka Tai","family":"Lau","affiliation":""},{"name":"Dan Wang","slug":"dan-wang","given":"Dan","family":"Wang","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2025/1074","url":"https://ijcai.org/proceedings/2025/1074","pdf":"https://ijcai.org/proceedings/2025/1074.pdf","markdown":"https://ijcai.org/proceedings/2025/1074.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1074","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI and Social Good","track":"AI and Social Good","number":1074,"pages":{"first":9665,"last":9673},"keywords":["Multidisciplinary Topics and Applications: General","Machine Learning: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}