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

abstract

Developing light-harvesting materials able to shape the sunlight to cope with the absorption region of photo-voltaic (PV) cells presents an opportunity for the utilization of spectral converters like the luminescent solar concentrators (LSCs). This study explores the use of artificial neural networks (ANNs) to predict the optical conversion efficiency of spectral converters, based on the material properties employed in their production, without the need for expensive and time-consuming experimental testing. To predict efficiency as a function of materials and manufacturing processes, ANNs were trained using data from previously documented physical implementations. The findings indicate that ANNs, having 97 and 19 neurons in the hidden layers, provide accurate efficiency predictions, making them a valuable tool for designing and optimizing spectral converting systems. The proposed model was validated and got a mean square error in the order of 10-5 for the optical conversion efficiency. The trained ANN introduced a novel methodology for predicting the optical efficiency of spectral converters, opening the door to the application of machine learning as a decision-making tool for ma-terial design, and eliminating the necessity for physical device implementations.

keywords

ENERGY; CELL

subject category

Energy & Fuels

authors

Andre, PS; Dias, LMS; Correia, SFH; Neto, ANC; Ferreira, RAS

Groups

G1 - Porous Materials and Nanosystems
G2 - Photonic, Electronic and Magnetic Materials

Projects

Dispositivos fotónicos para geração de energia a partir da luz solar e sensor de temperatura para avaliação pós-ocupação em tempo real e baseada no utilizador em edifícios de balanço energético nulo (PLANETa)

CICECO - Aveiro Institute of Materials (UIDB/50011/2020)

CICECO - Aveiro Institute of Materials (UIDP/50011/2020)

Solar-Powered Smart Windows for Sustainable Buildings (SOLPOWINS)

acknowledgements

This work is financed by Portugal 2020 through the European Regional Development Fund (ERDF) in the frame of CENTRO2020 in the scope of the project PLANETa, CENTRO -01-0247-FEDER-181242 and within the scope of the project CICECO-Aveiro Institute of Materials (UIDB/50011/2020 & UIDP/50011/2020) , Instituto de Telecomunicacoes (FCT Ref. UIDB/50008/2020) , SOLPOWINS-Solar- Powered Smart Windows for Sustainable Buildings (PTDC/CTM-REF/4304/2020) , financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership through European Regional Development Fund (ERDF) in the frame of Operational Competitiveness and Internationalization Programme (POCI) . SFHC and LMSD thank FCT (2022.03740.CEECIND and UI/BD/153491/2022, respectively) , and PSA acknowledges CMU Portugal for the mobility grant (CMU-P, Gestao 2020) . ANCN would like to acknowledge the support of the Shape of Water project (PTDC/NAN- PRO/3881/2020) funded by Portuguese funds through the FCT/MEC (PIDDAC) . T. Galvao (CICECO) is acknowledged for fruitful discussion about the ANN.