Deep Learning-Driven Real-Time Optimization for Improved Exergy Efficiency in Hybrid Renewable Energy Conversion Systems

Hybrid renewable energy systems that integrate solar, wind, and energy storage provide a promising solution for sustainable electricity generation. However, the intermittent nature of these resources often leads to inefficient energy conversion and increased thermodynamic losses, reducing exergy efficiency. Traditional rule-based control strategies struggle to adapt to rapidly changing environmental conditions. This study proposes an artificial intelligence–based framework that combines deep learning forecasting with reinforcement learning control to enhance system performance.

The proposed approach utilizes a Long Short-Term Memory (LSTM) neural network to predict short-term solar irradiance and wind speed using meteorological data from NASA POWER and historical records. These forecasts are integrated into a reinforcement learning agent based on the Proximal Policy Optimization (PPO) algorithm, which dynamically adjusts system parameters such as inverter operation, battery scheduling, and wind turbine pitch angle. The objective is to minimize exergy destruction while maintaining stable energy output.

The framework was evaluated using a simulation model of a hybrid solar–wind–battery system developed with Python-based tools, including TensorFlow. Exergy analysis was applied to quantify losses across system components. Validation was further conducted using a small-scale laboratory prototype comprising photovoltaic panels, a micro wind turbine, battery storage, and programmable converters.

Results indicate that the proposed approach achieves a noticeable improvement in exergy efficiency compared to conventional control strategies, primarily due to reduced losses in storage and conversion stages. These findings highlight the potential of AI-driven optimization for improving the efficiency and reliability of hybrid renewable energy systems.