Inspiration
What it does
This project focuses on predicting solar energy output using Support Vector Regression (SVR), a machine learning technique suited for regression problems. The model takes weather features like temperature, humidity, and cloud cover as inputs and predicts the amount of solar power generated. The goal is to improve grid efficiency, energy planning, and reliability in renewable energy systems. Diagnostic tools and error metrics like RMSE and MAE help evaluate and fine-tune the model’s performance.
