Hotspot diagnosis for solar photovoltaic modules using a Naive Bayes classifier

Kamran Ali Khan Niazi, Wajahat Akhtar, Hassan A. Khan, Yongheng Yang*, Shahrukh Athar

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

46 Citations (Scopus)


Monitoring and maintenance of photovoltaic (PV) modules are critical for a reliable and efficient operation. Hotspots in PV modules due to various defects and operational conditions may challenge the reliability, and in turn, the entire PV system. From the monitoring standpoint, hotspots should be detected and categorized for subsequent maintenance. In this paper, hotspots are detected, evaluated, and categorized uniquely by using a machine learning technique on thermal images of PV modules. To achieve so, the texture and histogram of gradient (HOG) features of thermal images of PV modules are used for classification. The categorized hotspots are detected by training the machine learning algorithm, i.e., a Naive Bayes (nBayes) classifier. Experimental results are performed on a 42.24-kWp PV system, which demonstrates that a mean recognition rate of around 94.1% is achieved for the set of 375 samples.

Original languageEnglish
JournalSolar Energy
Pages (from-to)34-43
Number of pages10
Publication statusPublished - 15 Sept 2019


  • Hotspots
  • Machine learning
  • Monitoring
  • Naive Bayes classifier
  • Photovoltaic (PV) modules
  • Texture and histogram of gradient (HOG) features
  • Thermal images
  • Thermographic assessment


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