Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing скачать в хорошем качестве

Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing

In this video, we explain the “Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs.” The study combines machine learning models (CatBoost, LightGBM, XGBoost) with hybrid metaheuristic optimization algorithms to design a cost-effective and reliable solar–battery–grid system. Unlike traditional methods, it uses 15-year forecasting data to account for long-term variability in solar generation and electricity demand. Key topics covered: ✔ Optimal sizing of solar panels and batteries ✔ Net-metering cost analysis ✔ Lifetime cost minimization (NPC) ✔ AI-driven renewable energy planning Perfect for students, researchers, and professionals working in renewable energy, microgrids, and energy optimization. #RenewableEnergy #MachineLearning #HybridEnergySystem #SolarEnergy #EnergyOptimization #NetMetering #Microgrid #Energy