AI-driven optimization of hybrid renewable energy systems: A review of techniques, challenges, and future direction

Abstract

The rapid advancement of Artificial Intelligence (AI) is opening up exciting new ways to improve hybrid renewable energy systems (HRES). These advancements promise greater efficiency, reliability, and sustainability in how we generate and use energy. In this review, we examine the current application of AI in HRES, focusing on techniques such as machine learning, deep learning, and reinforcement learning that facilitate energy use forecasting, resource management, and fault detection. However, integrating AI into these energy systems is not without its challenges. Issues such as data quality, the need for transparent algorithms, cybersecurity risks, and compatibility with older technologies can complicate things. Additionally, there are significant regulatory and ethical concerns to address, like algorithmic bias and ensuring inclusivity in AI applications, which can slow down widespread adoption. To tackle these obstacles, future research should focus on developing AI models that are easy to understand and explain. We also need systems that can adapt to changing market conditions and security frameworks that safeguard our cyber-physical infrastructures. Collaboration across disciplines, bringing together experts in data science, energy engineering, and environmental policy, will be crucial in building systems that are not only resilient but also tailored to specific contexts. Looking ahead, it is vital to include local communities and policymakers in the conversation to ensure that energy distribution is fair and that people trust AI-driven solutions. This review emphasizes that when used responsibly, AI can help drive a global shift toward more innovative, cleaner, and more inclusive energy systems.