Designing Machine Learning Solution to Optimize Hybrid Solar-Wind Energy System Performance: Impact of Wind Speed

Abstract

It is an open field of research to design an efficient hybrid system for Solar PV and Wind energy systems. The study demonstrates the use of machine learning (ML) techniques to optimize the design of a hybrid solar-wind energy system. This approach can lead to more efficient and cost-effective renewable energy solutions.The study designs a hybrid solar-wind energy system using MATLAB Simulation. It uses a grid search optimization (GSO) loop to find optimal PV panels and wind speed scaling factor, balancing energy contributions and evaluating performance using a scoring function. By optimizing the number of solar panels and wind speed characteristics, the study shows how to minimize energy deficits and maximize battery state of charge, resulting in a more reliable and efficient energy system. The optimization process takes into account local solar irradiance and wind speed profiles, allowing the system design to be tailored to specific geographical locations and environmental conditions.The system uses machine learning to optimize PV panels and wind speed, achieving a predicted energy deficit of 0.00 Wh and a surplus of 11,831.86 Wh, indicating strong generation capacity..