Special Issue: "AI Empowered Smart Clean Energy Systems", Discover Artificial Intelligence (Springer Nature), April 2025

The global shift towards sustainable energy is accelerating, driven by the urgent need to reduce carbon emissions and combat climate change. Central to this transition is the adoption of clean energy systems, which include renewable energy sources as well as advanced energy storage solutions. These systems are becoming increasingly sophisticated, requiring innovative approaches to optimize their performance, efficiency, and reliability.

Artificial intelligence (AI) has emerged as a game-changer in the energy sector, offering powerful tools to enhance the management and operation of clean energy systems. By leveraging machine learning, data analytics, and AI-driven optimization, we can significantly improve the efficiency of energy generation, storage, and distribution. AI empowers smart clean energy systems to predict and respond to energy demands in real-time, integrate diverse renewable sources seamlessly, and ensure the stability and resilience of the energy grid.
This collection aims to highlight the latest research and developments at the intersection of AI and clean energy. We seek contributions that showcase how AI can be harnessed to address key challenges in the energy sector, such as maximizing the output of renewable energy installations, optimizing the operation of energy storage systems, and enhancing the intelligence of smart grids. We also welcome studies that explore the environmental and economic impacts of AI-powered energy solutions, providing a comprehensive view of their benefits and potential drawbacks.

Topics of interest and focus in the collection include, but are not limited to:
- AI-driven optimization of renewable energy sources
- Machine learning algorithms for energy forecasting and load prediction
- Intelligent grid management and smart grid technologies
- AI in energy storage systems and battery management
- AI for energy efficiency and demand response
- Autonomous energy systems and AI-enabled control
- Predictive maintenance and fault detection in energy infrastructures
- Integration of AI with IoT in energy applications
- AI for environmental impact assessment and sustainability in energy systems

This Collection supports and amplifies research related to SDG 7.