resumo
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.
palavras-chave
CONVERSION
categoria
Science & Technology - Other Topics
autores
Ferreira, RAS; Correia, SFH; Fu, LS; Georgieva, P; Antunes, M; André, PS
nossos autores
Lianshe Fu
Investigador Auxiliar
Maria Rute de Amorim e Sá Ferreira André
Professor Catedrático
Sandra Filipa Henriques Correia
InvestigadorProjectos
CICECO - Aveiro Institute of Materials (UIDB/50011/2020)
CICECO - Aveiro Institute of Materials (UIDP/50011/2020)
Solar-Powered Smart Windows for Sustainable Buildings (SOLPOWINS)
agradecimentos
This work was supported by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project N0 BG-RRP-2.004-0005. 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 -01-0145-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-UIDP/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 thanks FCT (2022.03740.CEECIND/CP1716/CT0006).