An experimental analysis of photovoltaic panel performance using rice husk-derived cellulose nanocrystals: a response surface methodology based k-means clustering approach
摘要
Given the increasing demand for renewable energy and the need to improve solar cell efficiency, exploring sustainable materials like rice husk-based cellulose nanocrystals (CNCs) presents a promising avenue. This study aims to optimize the performance of solar cells coated with CNCs derived from rice husk. The operating parameters of direct normal irradiance (DNI), dry bulb temperature (DBT), and relative humidity (RH) will be varied to evaluate different outputs, including cell efficiency, durability, environmental impact, and cost-effectiveness. By employing k-means clustering and response surface methodology (RSM), the study identifies the best operating conditions that maximize solar cell efficiency. The research focuses on clustering solar cells based on their performance metrics and exploring the influence of CNC coating parameters on these clusters. Using k-means clustering, the optimal cluster group is cluster 3, while the optimal cluster for which the output settings are cell efficiency (7.2%), durability (2.5 years), reduced environmental impact (21%), and cost-effectiveness (20%). The optimal operating parameters came out to be for set 4, which has a minimum centroidal distance of 0.589 from the best cluster centroid. The optimization of a CNC-coated solar cell involves setting DNI at 8 W m-2 for efficient electricity generation, DBT at 40 °C for optimal electrical conductivity, and RH at 70% for a stable moisture level for heat dissipation. Through this approach, we aim to enhance the performance of solar cells, thereby contributing to the advancement of sustainable and high-efficiency solar energy technologies.