Energy harvesting is an effective solution, especially in scenarios with low power requirements, using sources such as magnetic fields, vibrations, and wind. This study focuses on predicting harvested power of toroidal electromagnetic energy harvesters using various machine learning methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Adaptive Boosting (AdaBoost), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest, Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). To enhance the performance of these models, Artificial Bee Colony (ABC) optimization has been applied. The experiments were conducted using 1,300 trials across seven toroidal cores with varying sizes and magnetic permeabilities. During each experiment, the line current was varied between 0–100 A, and the resulting induced voltage and current were recorded. These measurements were used to create a comprehensive dataset named the Toroidal-Energy-Harvesting Dataset, enabling accurate power prediction. The performance of the machine learning models was assessed using statistical metrics, including R², MSE, MAE, and RMSE. Among the evaluated models, the ABC-optimized XGBoost (ABC-XGBoost) demonstrated the highest performance, achieving an R² value of 0.9993, an MSE of 247.1, an MAE of 9.8, and an RMSE of 15.7, indicating superior accuracy and minimal error. The comparative analysis clearly shows that proposed ABC-XGBoost outperformed the other models, making it the most effective solution for accurate power prediction in the Toroidal-Energy-Harvesting Dataset.

Energy harvesting, Optimization, Artificial Bee Colony, Random Forest, XGBoost, LightGBM