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Automatic Damage Detection on Rooftop Solar Photovoltaic Arrays

Published: 18 November 2020 Publication History

Abstract

Homeowners may spend up to ~$375 to diagnose their damaged rooftop solar PV systems. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, current approaches may not reliably distinguish solar PV array damage from other degradation (e.g., shading, dust, snow). To address this issue, we design a new system---SolarDiagnostics that can automatically detect and profile damages on rooftop solar PV arrays using their rooftop images with a lower cost. We evaluate SolarDiagnostics by building a lower cost (~$35) prototype and using 60,000 damaged solar PV array images. We find that pre-trained SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews Correlation Coefficient of 0.95.

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Mahmoud Dhimish, Violeta Holmes, Bruce Mehrdadi, and Mark Dales. 2017. The impact of cracks on photovoltaic power performance. Journal of Science: Advanced Materials and Devices 2, 2 (2017), 199--209.
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Srinivasan Iyengar, Stephen Lee, Daniel Sheldon, and Prashant Shenoy. 2018. Solarclique: Detecting anomalies in residential solar arrays. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies. 1--10.
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Qi Li, Yuzhou Feng, Yuyang Leng, and Dong Chen. 2020. SolarFinder: Automatic Detection of Solar Photovoltaic Arrays. In Proceedings of the 19th ACM/IEEE International Conference on Information Processing in Sensor Networks. 100--111.
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Andreas Livera, Marios Theristis, George Makrides, and George E Georghiou. 2019. Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems. Renewable energy (2019).
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Cited By

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  • (2024)Holistic Energy Awareness and Robustness for Intelligent DronesACM Transactions on Sensor Networks10.1145/364185520:3(1-31)Online publication date: 23-Jan-2024
  • (2021)Holistic energy awareness for intelligent dronesProceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3486611.3486651(41-50)Online publication date: 17-Nov-2021

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BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
November 2020
361 pages
ISBN:9781450380614
DOI:10.1145/3408308
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 November 2020

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Author Tags

  1. Deep Learning
  2. Image Processing
  3. Machine Learning
  4. Solar Energy

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  • Poster
  • Research
  • Refereed limited

Funding Sources

  • Cyber Florida Collaborative Seed Program

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BuildSys '20
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BuildSys '20 Paper Acceptance Rate 38 of 139 submissions, 27%;
Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

View all
  • (2024)Holistic Energy Awareness and Robustness for Intelligent DronesACM Transactions on Sensor Networks10.1145/364185520:3(1-31)Online publication date: 23-Jan-2024
  • (2021)Holistic energy awareness for intelligent dronesProceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3486611.3486651(41-50)Online publication date: 17-Nov-2021

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