{"ID":2896680,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.07016","arxiv_id":"2507.07016","title":"On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence","abstract":"In this paper, an edge-side model training study is conducted on a resource-limited smart meter. The motivation of grid-edge intelligence and the concept of on-device training are introduced. Then, the technical preparation steps for on-device training are described. A case study on the task of photovoltaic power forecasting is presented, where two representative machine learning models are investigated: a gradient boosting tree model and a recurrent neural network model. To adapt to the resource-limited situation in the smart meter, \"mixed\"- and \"reduced\"-precision training schemes are also devised. Experiment results demonstrate the feasibility of economically achieving grid-edge intelligence via the existing advanced metering infrastructures.","short_abstract":"In this paper, an edge-side model training study is conducted on a resource-limited smart meter. The motivation of grid-edge intelligence and the concept of on-device training are introduced. Then, the technical preparation steps for on-device training are described. A case study on the task of photovoltaic power forec...","url_abs":"https://arxiv.org/abs/2507.07016","url_pdf":"https://arxiv.org/pdf/2507.07016v1","authors":"[\"Jian Huang\",\"Yongli Zhu\",\"Linna Xu\",\"Zhe Zheng\",\"Wenpeng Cui\",\"Mingyang Sun\"]","published":"2025-07-09T16:45:33Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SP\"]","methods":"[]","has_code":false}