Hydroelectric Power Potentiality Analysis for the Future Aspect of Trends with R2 Score Estimation by XGBoost and Random Forest Regressor Time Series Models

Abstract

This paper investigates the hydroelectric power trends in the future aspect using time series models- XGBoost & Random Forest Regressor. For estimating iterations on both models, a fixed epoch (500) is considered to analyze the performance based on the error parameters and r2 score. From the data analysis, it is seen that Random Forest Regressor has proven to be the better estimator obtaining r2 score of 0.962 than the XGBoost where r2 score is recorded as 0.926. Since hydroelectric power is harnessing the utmost prompt for mitigating the fossil fuel crisis, it is important to forecast the future aspect of this important energy profile. Hence a future aspect of hydroelectric power has been presented in this paper using both of these time series models.