A systematic review of artificial intelligence and machine learning in energy sustainability: Research topics and trends

The worldwide shift to sustainable energy is critical in addressing energy security, climate change, and socioeconomic challenges. This study examines the transformative impact of Machine Learning (ML) and Artificial Intelligence (AI) in optimizing energy systems and integrating renewable energy sources, emphasizing their pivotal role in attaining the United Nations Sustainable Development Goals. AI and ML applications, such as predictive analytics, demand forecasting, and smart grid management, reduce wastage, stabilize renewable energy variability, and enhance energy efficiency. Through a comprehensive analysis of existing literature, the study highlights the intersection of AI-driven innovation with renewable technologies like solar, wind, and bioenergy systems. It underscores how intelligent systems facilitate decentralized energy management, empowering consumers and promoting equity. However, integrating Artificial intelligence in sustainable energy systems poses challenges, including data privacy concerns, high computational requirements, and regulatory gaps. This work advocates for multidisciplinary collaboration among stakeholders to address these barriers and align energy innovations with sustainability goals. It emphasizes the importance of ethical considerations and inclusivity in deploying AI-driven energy solutions. By synthesizing insights across various fields, the paper elucidates the strategic potential of AI and ML in fostering resilient, adaptive, and inclusive energy systems, essential for navigating the complexities of a sustainable energy future.