Super-Resolution Reconstruction of Interval Energy Data

TL;DR

This paper introduces a deep learning-based super-resolution method to enhance low-resolution hourly energy data into higher-resolution 15-minute data, improving data utility for energy management applications.

Contribution

It proposes a novel super-resolution reconstruction approach specifically for interval energy data using deep learning, addressing the challenge of low-resolution data in energy systems.

Findings

Significant performance improvement over baseline models

Effective upsampling from hourly to 15-minute data

Potential for better energy data analysis and decision-making

Abstract

High-resolution data are desired in many data-driven applications; however, in many cases only data whose resolution is lower than expected are available due to various reasons. It is then a challenge how to obtain as much useful information as possible from the low-resolution data. In this paper, we target interval energy data collected by Advanced Metering Infrastructure (AMI), and propose a Super-Resolution Reconstruction (SRR) approach to upsample low-resolution (hourly) interval data into higher-resolution (15-minute) data using deep learning. Our preliminary results show that the proposed SRR approaches can achieve much improved performance compared to the baseline model.