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Grant support

This study was supported by the University of Valladolid with the predoctoral contracts of 2020, co-funded by Santander Bank.

Analysis of institutional authors

Mateo Romero, Hector FelipeCorresponding Author

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Article

Synthetic Dataset of Electroluminescence Images of Photovoltaic Cells by Deep Convolutional Generative Adversarial Networks

Publicated to:Sustainability. 15 (9): 7175- - 2023-04-25 15(9), DOI: 10.3390/su15097175

Authors: Mateo Romero, Hector Felipe; Hernandez-Callejo, Luis; Gonzalez Rebollo, Miguel Angel; Cardenoso-Payo, Valentin; Alonso Gomez, Victor; Jose Bello, Hugo; Moyo, Ranganai Tawanda; Morales Aragones, Jose Ignacio

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Abstract

Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Frechet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects.

Keywords

Artificial intelligenceClassificatioElectroluminescencElectroluminescenceGenerative adversarial neural networksModule cellsPhotovoltaicsSynthetic data

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sustainability due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Geography, Planning and Development.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 12.37, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-06, the following number of citations:

  • WoS: 11
  • Scopus: 17
  • OpenCitations: 12

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-06:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 31 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: South African Republic.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MATEO ROMERO, HECTOR FELIPE) .

    the author responsible for correspondence tasks has been MATEO ROMERO, HECTOR FELIPE.