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Super-Resolution Reconstruction of Interval Energy Data

2020-10-23 21:34:22
Jieyi Lu, Baihong Jin
arXiv_AI
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2010.12678

PDF

https://arxiv.org/pdf/2010.12678.pdf

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