Machine Learning-Based Optimization Techniques for Renewable Energy Systems

Abstract

Renewable energy systems are at the forefront of sustainable energy production, and machine learning-based optimization techniques have emerged as powerful tools to enhance their efficiency and output. This research paper investigates the application of Genetic Algorithms (GA), Neural Networks (NN), and Reinforcement Learning (RL) in optimizing various renewable energy systems, including solar photovoltaic (PV), wind turbines, and hybrid systems. The results demonstrate that machine learning-based optimizations significantly increase energy output. For solar PV systems, GA-based optimizations led to a 15% increase in energy output, resulting in an additional 6,570 kWh/year for a typical 5 kW system. Similarly, NN-based optimizations for wind turbines yielded a 12% increase, equivalent to an additional 525,600 kWh/year for a 10 MW wind farm. RL optimizations for hybrid systems led to a 20% increase in efficiency and a 10% reduction in energy storage requirements, contributing to substantial cost savings. The study also highlights environmental and economic benefits. The optimized renewable energy systems achieved a combined CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> reduction of approximately 1,200 kg/year, while cost savings from reduced maintenance and energy storage requirements reached $2,000 per year for a 10 MW wind farm and $1,500 per year for a typical hybrid system. Additionally, the RL-based optimization improved grid stability by reducing peak load stress and lowering the frequency of grid-related outages, contributing to enhanced reliability and adaptability.

Topics & Concepts