Artificial neural networks for predicting optical conversion efficiency in luminescent solar concentrators

André, P.S.; Dias, L.M.S.; Correia, S.F.H.; Carneiro Neto, A.N.; Ferreira, R.A.S.

doi:10.1016/j.solener.2023.112290

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…We note that the performance in terms of η opt is independent of the photovoltaic technology as it only quantifies the spectral conversion of the emitting layers, and thus this parameter can be predicted using ML algorithms, as recently shown using artificial neural networks 23 . Only the PCE values acquired with other semiconductor-based technologies (e.g.…”

Section: Resultsmentioning

confidence: 85%

“…3 ). The linear correlation between the optical features may be rationalized by attending to the typical Gaussian profile of the photoluminescence spectra, inducing the correlation between the E max and E p features 23 . The correlation between η opt and PCE arises due to the fact that the larger number of incident photons are converted (quantified by η opt ), the larger the probability to generate electrons in the photovoltaic cell (quantified by PCE).…”

Section: Resultsmentioning

confidence: 99%

“…The objective here is to discuss the use of ML as a valuable resource for decision-making tools for device design without extensive experimental measurements, as recently sugested 23 . A key step is the identification of the photoluminescence figures-of-merit related to performance (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning

Ferreira,

Correia,

et al. 2024

Sci Rep

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Building-integrated photovoltaics (BIPV) is an emerging technology in the solar energy field. It involves using luminescent solar concentrators to convert traditional windows into energy generators by utilizing light harvesting and conversion materials. This study investigates the application of machine learning (ML) to advance the fundamental understanding of optical material design. By leveraging accessible photoluminescent measurements, ML models estimate optical properties, streamlining the process of developing novel materials, offering a cost-effective and efficient alternative to traditional methods, and facilitating the selection of competitive materials. Regression and clustering methods were used to estimate the optical conversion efficiency and power conversion efficiency. The regression models achieved a Mean Absolute Error (MAE) of 10%, which demonstrates accuracy within a 10% range of possible values. Both regression and clustering models showed high agreement, with a minimal MAE of 7%, highlighting the efficacy of ML in predicting optical properties of luminescent materials for BIPV.

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

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Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning

Ferreira,

Correia,

et al. 2024

Sci Rep

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Perovskite Nanocrystals: Opportunities in Luminescent Solar Concentrators

Jin,

Selopal,

Liu

et al. 2024

Adv Funct Materials

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Luminescent solar concentrators (LSCs) are complementary sunlight collectors for photovoltaics (PVs). Emissive fluorophores embedded in a transparent waveguide collect solar radiation over a large area and convert it into luminescence directed to the PV cells that frame the waveguide's edges. Among various fluorophores, perovskite nanocrystals (PNCs) show considerable potential for LSCs thanks to their wide size/composition/shape tunable broad absorption spectrum ranging from UV to near‐infrared, which significantly overlaps with the solar spectrum. They also feature high brightness with a photoluminescence quantum yield of up to 100% and ease of fabrication through wet chemistry approaches. In addition, PNCs can be engineered to minimize the absorption/emission overlap, which is the key to suppressing energy losses caused by reabsorption. Here, the structure and properties of PNCs and then correlate them with LSC performance is presented. The synthesis of PNCs using wet‐chemistry approaches and summarize the latest developments of PNCs‐based LSCs, categorized by the engineering strategies of PNCs and the design of the LSC configurations is critically reviewed. Finally, it is described major challenges and perspectives for future work, outlining the rational design, synthesis, PNC loading, surface engineering, and machine‐learning‐based tuning of PNC‐LSC.

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Multi‐Surface Adhesion Luminescent Solar Concentrators for Supply‐Less IoT

Figueiredo,

Correia,

Falcão

et al. 2024

Advanced Science

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The growing prevalence of Internet of Things (IoT) devices hinges on resolving the challenge of powering sensors and transmitters. Addressing this, supply‐less IoT devices are gaining traction by integrating energy harvesters. This study introduces a temperature sensor devoid of external power sources, achieved through a novel luminescent solar concentrator (LSC) device based on a stretchable, adhesive elastomer. Leveraging a lanthanide‐doped styrene‐ethylene‐butylene‐styrene matrix, the LSC yielded 0.09% device efficiency. The resultant temperature sensor exhibits a thermal sensitivity of 2.1%°C−1 and a 0.06 °C temperature uncertainty, autonomously transmitting real‐time data to a server for user visualization via smartphones. Additionally, the integration of LED‐based lighting enables functionality in low‐light conditions, ensuring 24 h cycle operation and the possibility of having four distinct thermometric parameters without changing the device configuration, stating remarkable robustness and reliability of the system.

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Artificial neural networks for predicting optical conversion efficiency in luminescent solar concentrators

Cited by 5 publications

References 44 publications

Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning

Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning

Perovskite Nanocrystals: Opportunities in Luminescent Solar Concentrators

Multi‐Surface Adhesion Luminescent Solar Concentrators for Supply‐Less IoT

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