Paper Contents
Abstract
This study focuses on renewable energy output prediction using machine learning regression models in Python. Weather parameters such as temperature, humidity, wind speed, and solar radiation are analyzed to forecast energy generation. Multiple modelsincluding Linear, Ridge, Lasso, Polynomial, Random Forest, and Support Vector Regressionare compared using RMSE, MAE, and R metrics. Results show that ensemble methods, especially Random Forest, provide the highest accuracy, making them suitable for smart grids, energy planning, and sustainable development applications.
Copyright
Copyright © 2025 Naga vaishnavi .A. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS50900016793
ISSN:
2321-9653
Publisher:
ijprems