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.