arXiv：cs.LG· Jack Zheng, Hao Wang·· 11 小时前
智能电表数据下的户用光伏可见性：部署场景与运营洞察
Behind-the-Meter Photovoltaic Visibility from Net Smart Meters: Deployment Regimes and Operational Insights
AI 导读
研究基于 Ausgrid 智能电表数据并跨数据集适配 Pecan Street，用四种部署场景评估深度学习光伏分解：在固定 48 小时上下文窗口下，最优模型在已见家庭的未来测试期白天 MAE 为 0.104 kW，未见家庭升至 0.185 kW。在目标域归一化下，用五个目标区域家庭微调可将源模型迁移误差降低 28%–36%，且部署场景比模型选择更能解释误差差异。
正文
Abstract:Distribution operators need visibility of behind-the-meter photovoltaic (PV) generation, yet most residential customers are observed only through net smart meters. Deep-learning-based PV disaggregation can restore this visibility, but its operational value depends on whether the model serves known customers, new customers, future periods, or new regions. We quantify this dependence using four deployment regimes that separate household transfer from temporal extrapolation, applied to Ausgrid smart-meter data with cross-dataset adaptation to the Pecan Street data. Using a fixed training protocol, we benchmark representative deep sequence architectures for PV disaggregation under regime-matched evaluation. At a fixed 48-hour context length, daylight mean absolute error (MAE) of the best model rises from 0.104~kW on seen households in the future test period to 0.185~kW on unseen households over the same period, and under a fixed training contract the deployment regime explains far more of the error variation than model choice. Under target-domain normalization, fine-tuning on five target-region households reduces source-model transfer error by 28\%--36\%. Long input windows help known-customer extrapolation, while their new-customer benefits vary by model. These findings suggest regime-matched error budgets, limited target supervision for model adaptation in new regions, and use-case-dependent context selection for energy data analytics.
| Comments: | 6 pages, 5 figures, 6 tables. Accepted at the 10th IEEE Conference on Energy Internet and Energy System Integration (IEEE EI2 2026). IEEE Xplore publication pending |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12527 [cs.LG] |
| (or arXiv:2610.12527v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12527 arXiv-issued DOI via DataCite |
Submission history
From: Jack Zheng [view email]
[v1]
Thu, 8 Oct 2026 07:37:32 UTC (172 KB)
来源：arXiv：cs.LG · arxiv.org