Role of artificial neural networks in predicting design and efficiency of dye sensitized solar cells

Photovoltaic technology attracts researchers from industry and academia due to its potential in producing electricity directly from the sunlight. Among all the photovoltaic devices, the dye-sensitized solar cell has gained preference due to its low-cost fabrication and versatility in electrolytes, dye, substrate, and catalyst. The optical, electrical, and structural properties of the materials determine the power conversion efficiency of a solar cell. But, conducting experiments in the laboratory to identify the suitable materials for the fabrication of an efficient solar cell requires much time, cost, and human effort. The proven potential of machine learning techniques in pattern matching and computer vision motivated the researchers to employ these techniques for predicting the efficiency of solar cells. The research works conducted so far show the applications of these techniques in predicting the optimum efficiency, best suitable design, and material for the fabrication of Dye-Sensitized Solar Cells (DSSCs). In this paper, the authors present a comprehensive review of the machine learning techniques employed and the types of input data used for predicting the design and efficiency of solar cells. They also give essential insights into the selection of optimum parameters for selecting the materials for fabricating a substrate, dye sensitizer, semiconductor, electrolyte, and catalyst for designing the most efficient dye-sensitized solar cell without conducting experiments in the laboratory. This paper may prove a time and cost-saving assistant for developing a customized neural network model for predicting the efficiency of a DSSC from the dataset available in the literature. Highlights Artificial Neural Network (ANN) model is useful to identify the suitable materials for efficient DSSC assembly. The tailored neural network models minimize the need for hit and trial experiments. The hybrid of ANN and Genetic Algorithm (GA) offers a low-cost technological solution for DSSC assembly. The experimental data are vital for extracting useful insights for DSSC fabrication.

Automated summary

Photovoltaic technology is attracting researchers for its potential to generate electricity directly from sunlight. Among various photovoltaic devices, dye-sensitized solar cells are preferred due to their low-cost fabrication and versatility in materials. However, identifying suitable materials for efficient solar cell fabrication through laboratory experiments is time-consuming and costly. Researchers have started using machine learning techniques to predict the efficiency and design of solar cells. This paper presents a comprehensive review of the machine learning techniques and input data types used for predicting solar cell efficiency and design. It offers essential insights into selecting optimal parameters for material selection without the need for laboratory experiments, providing a time and cost-saving solution. The importance of experimental data for extracting useful insights for solar cell fabrication is emphasized.