论文标题
间隔能量数据的超分辨率重建
Super-Resolution Reconstruction of Interval Energy Data
论文作者
论文摘要
在许多数据驱动的应用程序中，需要高分辨率数据；但是，在许多情况下，仅由于各种原因，其分辨率低于预期的数据。然后，如何从低分辨率数据中获取尽可能多的有用信息是一个挑战。在本文中，我们针对通过高级计量基础架构（AMI）收集的间隔能量数据，并提出了一种超分辨率重建方法（SRR）方法，以使用深度学习来将低分辨率（小时）间隔数据提高到高分辨率（15分钟）数据。我们的初步结果表明，与基线模型相比，所提出的SRR方法可以提高性能。
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.
