Adaptive Forecasting Techniques for Load Variability in SEIG-ELC Off-Grid Systems using Machine Learning and Grid Search Optimization
Description
This dataset supports the research article “Adaptive Forecasting Techniques for Load Variability in SEIG-ELC Off-Grid Systems using Machine Learning and Grid Search Optimization.” It contains simulation data, machine parameters, excitation capacitance calculations, and performance metrics for multiple machine learning models—namely k-NN, Random Forest, ANN, and SVM—used for forecasting PCC voltage in a self-excited induction generator (SEIG) with an electronic load controller (ELC). Hyperparameter optimization, cross-validation results, training times, and feature importance analyses are included. The dataset aims to facilitate reproducibility and support further work in intelligent forecasting for off-grid micro-hydro systems. This dataset (file name Data_file (1)) contains machine learning–ready time-series data used to forecast load variability in Self-Excited Induction Generator (SEIG) based off-grid systems equipped with an Electronic Load Controller (ELC). The data was collected and simulated under varying load conditions to support model development and training for intelligent forecasting algorithms.
Files
Institutions
- National Institute of Technology Arunachal Pradesh