Prediction of Voltage Generation in Triboelectric ‎Nanogenerator Using Machine Learning Algorithms - A, Deepa; Nachimuthu, Loganathan; MV, Kavitha; D, Jyothi (International Journal of Basic and Applied Sciences, 2025)
The rapid evolution of solar panels towards greener energy has paved the way for eco-friendly renewable energy generation. However, the effective management of disposed solar cells is an important factor to consider in reducing adverse environmental and health consequences. Hence, ‎the novel based Triboelectric Nano generators are fabricated from waste solar cells and waste chocolate wrappers. The TENG harnesses ‎frictional energy from the contact between the materials, converting it into useful electrical power. This innovative system promotes the ‎efficient utilization of discarded resources, contributing to both renewable energy generation and waste reduction. As a result, the current work offers a realistic technique for gathering electricity and represents a major step in mitigating the difficulties associated with disposing of solar cell waste. The ‎output voltage generation by the TENG is predicted using various Machine learning algorithms. The predictive model performance is also ‎analyzed through various metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)‎.