Abstract
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 language | English |
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Title of host publication | 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings |
Publisher | IEEE |
Pages | 66-70 |
Number of pages | 5 |
ISBN (Print) | 9781538644102 |
DOIs | |
Publication status | Published - 17 Aug 2018 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Data Science Workshop - Lausanne, Switzerland Duration: 4 Jun 2018 → 6 Jun 2018 |
Workshop
Workshop | IEEE Data Science Workshop |
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Abbreviated title | DSW |
Country/Territory | Switzerland |
City | Lausanne |
Period | 04/06/2018 → 06/06/2018 |
Keywords
- Convolutional neural networks
- machine learning
- photovoltaic panels
- predictive maintenance
- wireless sensor networks