Harnessing nature and algorithms for green energy: the electrochemical property prediction of activated bamboo using data mining and metaheuristic tuning techniques

Abstract The application of Machine Learning (ML) models coupled with metaheuristic optimization algorithms represents a potentially powerful development in the field of predictive modeling, as it relates to sustainable energy materials. In this study, the electrochemical performance of biomass material from bamboo for energy storage applications is explored, focusing on the prediction of power density. The central objective is to enhance model accuracy with a novel hybrid model of Kernel Extreme Learning Machine (KELM) and Slime Mould Algorithm (SMA), Sunflower Optimization (SFO), and Social Ski Driver (SSD). The optimal predictive performance was achieved with KELM-SFO with a test Root Mean Square Error (RMSE) of 10,421.05, Mean Absolute Error (MAE) of 4,654.07, and R-squared (R 2 ) of 96.2 %. Early and fast plateauing of the SFO algorithm’s convergence curve indicated stable, early-stage optimization. In addition to filling a significant knowledge gap in ML-incorporated materials modeling, this work opens the door for future research on deep learning techniques, adaptive hybrid optimization algorithms, and real-time experimental validations to improve the electrochemical prediction efficiency in energy storage systems inspired by biomass.