Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer

TL;DR

This paper presents a novel self-supervised generative adversarial transformer approach for energy data super-resolution that improves accuracy without needing high-resolution ground truth data, outperforming traditional methods.

Contribution

Introduces a self-supervised GAT-based method for energy data super-resolution that avoids reliance on ground-truth high-resolution data, addressing fundamental limitations of existing models.

Findings

Reduces RMSE of upsampling by 10% compared to conventional methods.

Improves MPC application accuracy by 13%.

Operates without ground-truth high-resolution data.

Abstract

To bridge the temporal granularity gap in energy network design and operation based on Energy System Models, resampling of time series is required. While conventional upsampling methods are computationally efficient, they often result in significant information loss or increased noise. Advanced models such as time series generation models, Super-Resolution models and imputation models show potential, but also face fundamental challenges. The goal of time series generative models is to learn the distribution of the original data to generate high-resolution series with similar statistical characteristics. This is not entirely consistent with the definition of upsampling. Time series Super-Resolution models or imputation models can degrade the accuracy of upsampling because the input low-resolution time series are sparse and may have insufficient context. Moreover, such models usually rely on supervised learning paradigms. This presents a fundamental application paradox: their training requires the high-resolution time series that is intrinsically absent in upsampling application scenarios. To address the mentioned upsampling issue, this paper introduces a new method utilizing Generative Adversarial Transformers (GATs), which can be trained without access to any ground-truth high-resolution data. Compared with conventional interpolation methods, the introduced method can reduce the root mean square error (RMSE) of upsampling tasks by 10%, and the accuracy of a model predictive control (MPC) application scenario is improved by 13%.