在智能电表上实现光伏功率预测模型的本地训练，推动电网边缘智能。
On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence
- 在资源受限的智能电表上开展模型本地训练，支持边缘智能。
- 混合与低精度训练方案使模型在有限算力下仍可有效运行。
- 验证了现有智能电表基础设施实现边缘智能的可行性。
本文在资源受限的智能电表上开展边缘侧模型训练研究，阐述了电网边缘智能的动机与设备端训练的概念，并介绍了技术准备步骤。以光伏功率预测任务为例，研究了梯度提升树和循环神经网络两种代表性机器学习模型。为适应智能电表的资源限制，提出了“混合”和“降低”精度的训练方案。实验结果表明，通过现有高级计量基础设施，可经济地实现电网边缘智能。
原文摘要 · Abstract (English)
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.
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