PV Array Soiling Detection using Machine Learning

Joshua Martin, Kristen Jaskie, Yiannis Tofis, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Solar panel soiling detection is an important problem as soiled panels produce substantially reduced energy. This paper describes a collaborative project between Arizona State University and the University of Cyprus on fault detection. The project is part of an NSF program called International Research Experiences for Students on using machine learning for energy applications. In this study, we focus specifically on two methods for identifying soiling in residential solar installations. The first method aims to calculate a daily energy-lost-due-to-soiling value by comparing two calculated power curves: the expected best case scenario curve and a weather corrected curve, which estimates what the day's power curve would be in the absence of cloud cover. The second method compares the performance of sites in the same weather region using a multi-level k-means clustering strategy. Initial results with ground truth feedback suggest that this second method is effective. The key take-away from this study is that these methods do not require feature rich datasets, which are often unavailable, rather they operate solely on time-series power values.

Original languageEnglish (US)
Title of host publicationIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665400329
DOIs
StatePublished - Jul 12 2021
Event12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021 - Virtual, Chania Crete, Greece
Duration: Jul 12 2021Jul 14 2021

Publication series

NameIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications

Conference

Conference12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021
Country/TerritoryGreece
CityVirtual, Chania Crete
Period7/12/217/14/21

Keywords

  • Photovoltaic (PV) systems
  • fault detection
  • k-means
  • machine learning
  • renewable energy.
  • residential solar
  • soiling
  • solar monitoring

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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