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This paper applies conditional flow matching, a generative ML method, to smart meter data, so one trained model can handle synthetic generation, imputation, and super-resolution through inference-time projection guidance rather than task-specific retraining. The reported gains are modest and benchmark-specific (a 4.5% CRPS improvement on one public dataset, with parity on imputation), so the main takeaway for ML readers is the single-model, guidance-based approach to conditional generation under hard constraints. Grid operators will care more about whether it holds up on their own data.
SmartMeterFM: Flow Matching for Smart Meter Data Generation, Imputation, and Super-Resolution Without Retraining
arXiv:2601.21706v3 Announce Type: replace Abstract: Distribution system operators need realistic load and generation profiles when measurements are unavailable, incomplete, or too coarse for the intended analysis. Training separate models for synthetic data generation, imputation, and super-resolution can duplicate modeling effort as observation requirements change. This paper presents SmartMeterFM, a conditional flow matching model trained once on monthly smart meter profiles at 15 min resolution. Customer and calendar attributes condition the model during training, while numerical profile constraints, observed intervals, and coarse measurements are incorporated through projection-based guidance at inference. The same weights serve all tasks without task-specific retraining and preserve temporal patterns and extreme values while producing profiles consistent with the supplied observations. A large-scale case study using Dutch consumption and photovoltaic generation profiles reports lower mean Continuous Ranked Probability Score (CRPS) than the selected task-specific baselines for imputation and 16-fold super-resolution. On a public dataset of 2,400 residential meters, the same weights match the imputation CRPS of a task-trained diffusion model when sampled ancestrally, attain a 4.5% lower super-resolution CRPS with the default sampler, and meet the specified monthly maximum and mean power within 0.01 kW.
Abstract:Distribution system operators need realistic load and generation profiles when measurements are unavailable, incomplete, or too coarse for the intended analysis. Training separate models for synthetic data generation, imputation, and super-resolution can duplicate modeling effort as observation requirements change. This paper presents SmartMeterFM, a conditional flow matching model trained once on monthly smart meter profiles at 15 min resolution. Customer and calendar attributes condition the model during training, while numerical profile constraints, observed intervals, and coarse measurements are incorporated through projection-based guidance at inference. The same weights serve all tasks without task-specific retraining and preserve temporal patterns and extreme values while producing profiles consistent with the supplied observations. A large-scale case study using Dutch consumption and photovoltaic generation profiles reports lower mean Continuous Ranked Probability Score (CRPS) than the selected task-specific baselines for imputation and 16-fold super-resolution. On a public dataset of 2,400 residential meters, the same weights match the imputation CRPS of a task-trained diffusion model when sampled ancestrally, attain a 4.5% lower super-resolution CRPS with the default sampler, and meet the specified monthly maximum and mean power within 0.01 kW.
Machine Learning (cs.LG); Systems and Control (eess.SY)
arXiv:2601.21706 [cs.LG]
(or arXiv:2601.21706v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.21706 Focus to learn more arXiv-issued DOI via DataCite
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