Clean Energy Prediction Using Support Vector Machines
This research explores clean energy prediction using Support Vector Machines (SVM) with a focus on robust forecasting under changing and noisy real-world conditions. The work emphasizes strong feature design, model tuning, and reliable evaluation so that prediction quality remains stable across different demand and supply patterns.
The core objective is to improve forecast consistency and reduce prediction error for clean energy planning workflows. The analysis compares model behavior under varied data splits and parameter settings to validate deployment readiness.