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An Open Problem: Energy Data Super-Resolution

Published: 18 November 2020 Publication History

Abstract

In this notes paper, we present an open problem to the Buildsys community: energy data super-resolution, referring to the task of estimating the power consumption of a home at a higher resolution given the low-resolution power consumption. Super-resolution is especially useful when the smart meters collect data at a very low-sampling rate owing to a plethora of issues such as bandwidth, pricing, old hardware, among others. The problem is motivated by the success of image super resolution in the computer vision community. In this paper, we formally introduce the problem and present baseline methods and the algorithms we used to "solve" this problem. We evaluate the performance of the algorithms on a real-world dataset and discuss the results. We also discuss what makes this problem hard and why a trivial baseline is hard to beat.

References

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Nipun Batra, Yiling Jia, Hongning Wang, and Kamin Whitehouse. 2018. Transferring decomposed tensors for scalable energy breakdown across regions. In Thirty-Second AAAI Conference on Artificial Intelligence.
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Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2014. Learning a deep convolutional network for image super-resolution. In European conference on computer vision. Springer, 184--199.
[3]
Oliver Parson, Grant Fisher, April Hersey, Nipun Batra, Jack Kelly, Amarjeet Singh, William Knottenbelt, and Alex Rogers. 2015. Dataport and nilmtk: A building data set designed for non-intrusive load monitoring. In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 210--214.
[4]
Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition. 815--823.
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Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel Goddard, and Charles Sutton. 2018. Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Thirty-second AAAI conference on artificial intelligence.

Cited By

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  • (2021)M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption EnvironmentsEnergies10.3390/en1416476514:16(4765)Online publication date: 5-Aug-2021
  • (2021)Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric PerspectiveEnergies10.3390/en1403071914:3(719)Online publication date: 30-Jan-2021

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  1. An Open Problem: Energy Data Super-Resolution

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    cover image ACM Other conferences
    NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring
    November 2020
    109 pages
    ISBN:9781450381918
    DOI:10.1145/3427771
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 18 November 2020

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    Author Tags

    1. energy analytics
    2. smart meters
    3. super-resolution

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    BuildSys '20
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    View all
    • (2021)M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption EnvironmentsEnergies10.3390/en1416476514:16(4765)Online publication date: 5-Aug-2021
    • (2021)Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric PerspectiveEnergies10.3390/en1403071914:3(719)Online publication date: 30-Jan-2021

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