Predictive Maintenance of Photovoltaic Panels via Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

20 Citations (Scopus)


We apply convolutional neural networks (CNN) for monitoring the operation of photovoltaic panels. In particular, we predict the daily electrical power curve of a photovoltaic panel based on the power curves of neighboring panels. An exceptionally large deviation between predicted and actual (observed) power curve can be used to indicate a malfunctioning panel. The problem is quite challenging because the power curve depends on many factors such as weather conditions and the surrounding objects (causing shadows with a regular time pattern). We demonstrate, by means of numerical experiments, that the proposed method is able to predict accurately the power curve of a functioning panel. Moreover, the proposed approach outperforms the existing approaches that are based on simple interpolation filters.

Original languageEnglish
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Number of pages5
ISBN (Print)9781538644102
Publication statusPublished - 17 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Data Science Workshop - Lausanne, Switzerland
Duration: 4 Jun 20186 Jun 2018


WorkshopIEEE Data Science Workshop
Abbreviated titleDSW


  • Convolutional neural networks
  • machine learning
  • photovoltaic panels
  • predictive maintenance
  • wireless sensor networks


Dive into the research topics of 'Predictive Maintenance of Photovoltaic Panels via Deep Learning'. Together they form a unique fingerprint.

Cite this