SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models

TL;DR

This paper introduces a unified flow matching model for various smart meter data generation tasks, such as imputation and super-resolution, improving efficiency and data quality without re-training for each task.

Contribution

It proposes a novel flow matching approach that unifies multiple smart meter data generative tasks into a single conditional model, reducing redundancy.

Findings

Model generates high-dimensional time series data effectively.

Unified model outperforms task-specific baselines.

Generated data is consistent and realistic.

Abstract

Smart meter data is the foundation for planning and operating the distribution network. Unfortunately, such data are not always available due to privacy regulations. Meanwhile, the collected data may be corrupted due to sensor or transmission failure, or it may not have sufficient resolution for downstream tasks. A wide range of generative tasks is formulated to address these issues, including synthetic data generation, missing data imputation, and super-resolution. Despite the success of machine learning models on these tasks, dedicated models need to be designed and trained for each task, leading to redundancy and inefficiency. In this paper, by recognizing the powerful modeling capability of flow matching models, we propose a new approach to unify diverse smart meter data generative tasks with a single model trained for conditional generation. The proposed flow matching models are trained to generate challenging, high-dimensional time series data, specifically monthly smart meter data at a 15 min resolution. By viewing different generative tasks as distinct forms of partial data observations and injecting them into the generation process, we unify tasks such as imputation and super-resolution with a single model, eliminating the need for re-training. The data generated by our model not only are consistent with the given observations but also remain realistic, showing better performance against interpolation and other machine learning based baselines dedicated to the tasks.