On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912473146195968 |
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| author | Huang, Jian Zhu, Yongli Xu, Linna Zheng, Zhe Cui, Wenpeng Sun, Mingyang |
| author_facet | Huang, Jian Zhu, Yongli Xu, Linna Zheng, Zhe Cui, Wenpeng Sun, Mingyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence Huang, Jian Zhu, Yongli Xu, Linna Zheng, Zhe Cui, Wenpeng Sun, Mingyang Machine Learning Signal Processing 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. |
| title | On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2507.07016 |